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ICSB 2015 : 17th International Conference on Systems Biology

Paris, France
September 21 – 22, 2015

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International Conference Committee

Muhammad Shuaib   Institute of Genetics Molecular and Cellular Biology (igbmc), FR
Huseyin Seker   De Montfort University, UK
Kal Ramnarayan   Sapient Discovery, US
Oleg Bougri   Agrivida Inc., US
Mohan Boggara   Rensselaer Polytechnic Institute, US
Robert Weinberg   Massachusetts Institute of Technology, US
Jannavi Srinivasan   U. S. Food and Drug Administration, US
Adarsh Ramakumar   Armed Forces Radiobiology Researach Institute, US
Erich Baker   Baylor University, US
Zhi Wei   New Jersey Institute of Technology, US
Srinivas Pentyala   Stony Brook Medical Center, US
Mohamed Ahmed Elbaz   Ohsu Ogi-dse, US
S. M. Shahinul Islam   University of Rajshahi, BD
Hasan Basri Jumin   Islamic University of Riau Indonesia, ID
Kannan Kialvan Packaim   Bannari Amman Institute of Technology, IN
Tapas Ranjan Das   Indian Agricultural Research Institute, IN
Pragati Misra   Sam Higgin Bottom Institute of Agriculture Technology and Sciences, IN
Mohamed Boudjelal   King Abdullah International Medical Research Center, SA
Devasena Thiyagarajan   Anna University, IN
Khuda Bakhsh Bakhsh   University of Agriculture, Faisalabad, PK
Isaac Olusanjo Adewale   Obafemi Awolowo University, NG
Zakira Naureen   University of Nizwa, OM
Angzzas Kassim   Universiti Tun Hussein Onn Malaysia, MY
Hossein Vaheed   Tarbiat Modares University, IR
Adnan Kanbar   Damascus University, SY
Anusha. Morur Balakrishnan   Srm University, IN
Geetha Karuppasamy   Kamaraj College of Engineering & Technology, IN
Mehrnoush Amid   University Putra Malaysia, MY
Amolnat Tunsirikongkon   Thammasat University, TH
Manoj Parakhia   Junagadh Agricultural University, IN
Sivakumar Durairaj   Vel Tech High Tech Dr.rangarajan Dr.sakunthala Engineering College, IN
Tsung-Lu Michael Lee   Kun Shan University, TW
Ramesh H. L.   V.v.pura Science College, Bangalore University, IN

Biomedical Text Mining and Its Applications

Introduction

This tutorial is intended for biologists and computational biologists interested in adding text mining tools to their bioinformatics toolbox. As an illustrative example, the tutorial examines the relationship between progressive multifocal leukoencephalopathy (PML) and antibodies. Recent cases of PML have been associated to the administration of some monoclonal antibodies such as efalizumab [1]. Those interested in a further introduction to text mining may also want to read other reviews [2][4].

Understanding large amounts of text with the aid of a computer is harder than simply equipping a computer with a grammar and a dictionary. A computer, like a human, needs certain specialized knowledge in order to understand text. The scientific field that is dedicated to train computers with the right knowledge for this task (among other tasks) is called natural language processing (NLP). Biomedical text mining (henceforth, text mining) is the subfield that deals with text that comes from biology, medicine, and chemistry (henceforth, biomedical text). Another popular name is BioNLP, which some practitioners use as synonymous with text mining.

Biomedical text is not a homogeneous realm [5]. Medical records are written differently from scientific articles, sequence annotations, or public health guidelines. Moreover, local dialects are not uncommon [6]. For example, medical centers develop their own jargons and laboratories create their idiosyncratic protein nomenclatures. This variability means, in practice, that text mining applications are tailored to specific types of text. In particular, for reasons of availability and cost, many are designed for scientific abstracts in English from Medline.

Main Concepts

Terms

A term is a name used in a specific domain, and a terminology is a collection of terms. Terms abound in biomedical text, where they constitute important building blocks. Some examples of terms are the names of cell types, proteins, medical devices, diseases, gene mutations, chemical names, and protein domains [7]. Due to their importance, text miners have worked to design algorithms that recognize terms (see examples in Figure 1). The task of recognizing terms is also called named entity recognition in the text mining literature, although this NLP task is broader and goes beyond recognition of terms. Although the concept of term is intuitive (or, perhaps, because it is intuitive), terms are hard to define precisely [8]. For example, the text “early progressive multifocal leukoencephalopathy” could possibly refer to any, or all, of these disease terms: “early progressive multifocal leukoencephalopathy,” “progressive multifocal leukoencephalopathy,” “multifocal leukoencephalopathy,” and “leukoencephalopathy.” To overcome such dilemmas, text miners ask experts to identify terms within collections of text such as sets of selected Medline abstracts. These annotations are then used to train a computer by example, so that the computer can emulate the knowledge experts deploy when they read biomedical text. This pedagogical method, “teaching by example,” is a common approach used in many text mining tasks and it is more generally called supervised training. (Alternatively, text miners create rules using expert knowledge.) Thus, text miners rely heavily on collections of text (corpora) that have been annotated by experts (see compilations of corpora: http://www2.informatik.hu-berlin.de/~ hakenber/links/benchmarks.html;http://compbio.uchsc.edu/ccp/corpora/obt​aining.shtml). Before beginning a text mining task, it is advisable to limit the scope of the task to a corpus made of a set of documents around the topic of interest. In our case, a PML corpus could comprise all the Medline abstracts that mention the term “progressive multifocal leukoencephalopathy,” because this is an unambiguous term. Another relevant corpus to consider could be the ImmunoTome [9], which is focused on immunology.

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Figure 1. Examples of term recognition.

(A) Text marked with protein (blue), disease (crimson), Gene Ontology (bright red), chemical (dark red), and species (red) terms by Whatizit [15] with thewhatizitEBIMedDiseaseChemicals pipeline. (B) Text marked with protein and cell line terms by ABNER [16]. (C) Protein terms identified by the prototype BIOCreAtIvE metaserver [68]. In the example shown, the metaserver combines the output of systems hosted in three servers.

doi:10.1371/journal.pcbi.1000597.g001

Text miners are interested in terminologies that have been built manually. These controlled terminologies have notable roles in biomedicine, for example, the HUGO gene nomenclature, the ICD disease classification, or the Gene Ontology. Many of these terminologies are more than just a flat list of terms. Some include term synonyms (thesauri) or relations between terms (taxonomies, ontologies). For text miners, their usefulness comes from their ability to link to information. Once a text is mapped to one of these terminologies, a bridge is opened between the text and other resources. This usefulness justifies efforts such as the National Library of Medicine’s manual mapping of Medline abstracts to the Medical Subject Headings (MeSH) terminology. In our example, MeSH can be used to make the PML corpus more focused by restricting it only to abstracts with the MeSH term “leukoencephalopathy, progressive multifocal.” Controlled terminologies can be used to annotate results from experiments and databases [10]. Text miners attempt to make such mappings automatically. For example, a task called gene normalization consists in recognizing names of genes in text and mapping them to their corresponding gene identifiers (e.g., Entrez Gene ID). Thus, using gene normalization it is possible to identify all the abstracts in Medline that mention a given gene from Entrez Gene[11].

Because there are many controlled terminologies, some terminologies have been created to map between them. For example, the BioThesaurus [12] is a compilation of protein synonyms from several terminologies. The Unified Medical Language System (UMLS) [13],[14] is a grand compilation of more than 120 terminologies and close to 4 million terms. Despite UMLS’s size, all controlled terminologies are incomplete, because new terms are created too quickly to keep them up to date. Furthermore, all have gaps and areas of emphasis that conflict with the needs of users.

Tools for Terms

Whatizit [15] is a tool that recognizes several types of terms. It can be accessed through a Web interface, Web services, or a streamed servlet. Abner [16] is a standalone application that recognizes five types of terms: protein, DNA, RNA, cell line, and cell type. More specialized term recognition has been used, for example, for databases such as LSAT [17] for alternative transcripts and PepBank [18] for peptides. Text miners have also used terminologies to enrich PubMed’s search capabilities. Some recent search engines are semedico [19], novo|seek [20], and GoPubMed/GoGene [21],[22].

Relationships

After recognizing terms, the natural next step is to look for relationships between terms. The simplest method to identify relationships is using the co-occurrence assumption: terms that appear in the same texts tend to be related. For example, if a protein is mentioned often in the same abstracts as a disease, it is reasonable to hypothesize that the protein is involved in some aspect of the disease. The degree of co-occurrence can be quantified statistically to rank and eliminate statistically weak co-occurrences (see Box 1). An example using GoGene [22]can illustrate the use of simple co-occurrence, MeSH terms, and gene normalization. The query“leukoencephalopathy, progressive multifocal”[mh] in GoGene returns all the genes mentioned in Medline abstracts annotated with the MeSH term for PML. The genes that appear most often are likely to be related to PML. Those that appear disproportionately more often for PML than for other diseases are likely to be more specific to PML.

Box 1. The strength of a relationship. The confidence in a fact that comes from text can be qualified by the level of certainty of the assertion where the fact was found or by the strength of the evidence pointed [71]. Since facts do not stand alone, this confidence depends also on the fact’s consistency with related facts [72]. In the case of co-occurrence of two terms t1 and t2, the simplest confidence metric is the count c of texts that include both terms, (for a PPI example, see [73]). This measure can be normalized by the possibility of random co-occurrences due to the sheer popularity of one or both terms. For example,Pointwise mutual information (PMI) is similarly derived aswhere , in this case, is divided by the total number of texts. More generally, different measures can be drawn from the 2×2 contingency table that encompasses the counts of texts that include the two terms, , only one term ( and ), and none, . Using this contingency table, Medgene [32]compared the merit of different statistical measures for gene-disease associations such as chi-square analysis, Fisher’s exact probabilities, relative risk of gene, and relative risk of disease. More heuristic methods have been devised that use manually adjusted weights for different types of co-occurrence [36].

Better evidence than co-occurrence comes from relationships that are described explicitly [23]. For example, the sentence “We describe a PML in a 67-year-old woman with a destructive polyarthritis associated with anti-JO1 antibodies treated with corticosteroids” [24] describes an explicit link between PML and anti-JO1 antibodies. We can simplify this relationship into a triplet of two terms and a verb: PML is associated with anti-JO1 antibodies. To create the triplet, the verb can be identified with the aid of a part-of-speech (POS) tagger. An example of a POS tagger for biomedical text is MedPost [25]. This triplet representation is powerful due to its simplicity, but it omits crucial details from the original article, such as the fact that the evidence comes from a clinical case study.

A heavily studied area in text mining concerns the relationships known as protein-protein interactions (PPI). Using the triplet representation, PPI can be depicted as network graphs with the proteins as nodes and the verbs as edges (see Figure 2). When analyzing text-mined interaction networks, it is important to understand the information that underpins them. For example, interactions can be direct (physical) or indirect, depending on the verb (examples of direct verbs are to bind, to stabilize, to phosphorylate; examples of indirect verbs are to induce,to trigger, to block) [26]. The different nature of the protein interactions described in the literature reflects in part the experimental methodology employed and the nature of the interaction itself. A common way to capture the textual variations is by exhaustively identifying all the patterns that appear and writing a set of rules that capture them [27],[28]. For example, a simple pattern to capture phosphorylations might involve, sequentially, a kinase name, a form of the verb to phosphorylate, and a substrate name [29],[30].

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Figure 2. Example of text-mined PPI network.

The nodes are proteins identified using the query: “leukoencephalopathy, progressive multifocal”[mh] antibody[pubmed] in GoGene [22]. The query retrieves gene symbols mapped to PubMed abstracts that include the keyword antibody and the MeSH termleukoencephalopathy, progressive multifocal (PML). The gene list was exported to SIF format and the gene symbols extracted and used to query PPI using iHOP Web services[69]. Only those iHOP interactions with at least two co-occurrences and confidence above zero were considered. The network was plotted using Cytoscape [70]. The node color is based on the number of interactions (node degree).

doi:10.1371/journal.pcbi.1000597.g002

Tools for Relationships

To see co-occurrence in action, try FACTA [31]. MedGene and BioGene [32],[33] use co-occurrence for gene prioritization. Gene prioritization tools such as Endeavour [34] and G2D[35] use text as well as other data sources. PolySearch [36] uses heuristic weighting of different co-occurrence measures and includes a detailed guide to implementation and vocabularies. Anni [37] uses textual profiles instead of co-occurrence to measure relationship between terms. For PPI, iHOP [38] is the most popular tool. RLIMS-P [30] uses linguistic patterns to detect the kinase, substrate, and phosphosite in a phosphorylation. E3Miner [39] detects ubiquitinations, including contextual information.

Discovery

Besides finding relationships, text miners are also interested in discovering relationships. Due to the size of the literature, scientists miss links between their work and other, related work. Swanson called these links “undiscovered public knowledge.” In a classic example he found by careful reading 11 links between magnesium and migraine that had been neglected [40]. One method to discover relationships is based on transitive inference [41]. Simply stated, if A is linked to B, and B is linked to C, then there is a chance that A is linked to C. PPI networks are, at the core, an example of transitive inference. Arrowsmith [42] is a basic discovery tool that compares two literature sets to find links between them. Applying Arrowsmith to the literature for PML and antibodies yields the immunomodulator tacrolimus, a calcineurin inhibitor, among the top hits. Tacrolimus affects the production of several proteins depicted in Figure 2, such as IL-2.

Quality

The most common measure of output quality in text mining is the F-measure, which is the harmonic mean of two other measures, precision and recall. These three measures can be described with the analogy of searching for needles in a haystack. After a manual search of a haystack, our hands end up full with valuable needles but also with some useless straws. Recall is based on the number of needles found. High recall means that we have found most of the needles for which we were looking. Precision, however, is based on the number of both needles and straws. High precision means that we have retrieved far more needles than straws. Both high precision and high recall are desirable, and a high F-measure reflects both because it is the harmonic mean. Optimizing the F-measure of a text mining application is often different from optimizing the accuracy, because there are usually few needles and large amounts of hay in the haystack. An application that identifies the whole haystack as being only hay is quite accurate but misses all the needles.

It is important to ponder over the way an application has been evaluated before assessing its F-measure [43], and especially to consider how realistic the evaluation was. The F-measure is not an absolute value. The larger a haystack is, the more difficult it is to find needles. In other words, a low F-measure might reflect a harder task, not a worse application. Moreover, text mined applications may perform differently in different types of text and this may be reflected in lower F-measures than advertised. When the F-measure attainable is not high enough, one solution is to use text mining as a filter. A filter needs high recall, but only moderate precision, to reduce the amount of hay without affecting the needles. Filtering with text mining is used as a preliminary step in databases such as MINT [44], DIP [45], and BIND [46]. Filtering is followed by human curation, which involves the review and assessment of results to reduce hay and, hopefully, provide feedback to improve the filtering. The feedback loop between text mining and curation can have an incremental positive impact in output results [47].

Comprehensiveness

Doing comprehensive text mining means considering all sources of information—Medline and beyond. The abstract conveys an article’s main findings, but many other pieces of information are elsewhere in the full text, figures, tables, supplementary information, references, databases, Web sites, and multimedia files. In particular, the full text is critical for information that rarely appears in abstracts, such as experimental measurements. A more comprehensive PML corpus would include full text articles, however despite the surge in open access articles (see the Directory of Open Access Journals, www.doaj.org; [48]), the majority of published articles have access and processing restrictions. PubMed Central [49] is the main source of open access articles, and the specialized search engines BioText [50], Yale Image Finder [51], and Figurome[52] search PubMed Central figures and tables. A search for “progressive multifocal leukoencephalopathy” in the Yale Image Finder yields only one figure, while a search for “PML” yields a large number of hits, most of them not relevant because PML is an ambiguous acronym.

Text and DNA

Considering text as a sequence of symbols as informative as a protein’s DNA sequence is the underlying premise of many text mining tools for bioinformatics. For example, the linguistic similarity between protein corpora (sets of texts built around proteins) correlates with the BLAST score between those same proteins [53]. Text that is used in articles or database annotations to describe a protein can be used for protein clustering and to predict structure [54], subcellular localization, and function [55]. For example, a protein corpus of a protein located in the nucleus uses a vocabulary that is somewhat different from a corpus built around a secreted protein. These vocabulary differences can be used to predict the subcellular localization of a protein of unknown location. One way to measure vocabulary differences is to represent the texts as vectors of word counts. The word counts can be normalized by the size of the text they come from and the vectors compared using, for example, Euclidean distance (for more, see[56]). To reduce vector dimensionality, some words can be grouped using a method called stemming. A simple example of stemming is converting plural nouns into singular form and verbs into infinitive form (a widely used stemming algorithm is the Porter stemmer [57]). Additional simplification can be achieved via tokenization, because some words can be separated into constitutive elements called tokens. In English, however, most words are a single token. An example of a word of two tokens is don’t.

Text mining applications for bioinformatics [58] include subcellular localization prediction such as Sherloc and Epiloc [59],[60] and protein clustering such as TXTGate [61]. Thus, text mining tools can be used for annotating biological databases in the same fashion other bioinformatics tools are used.

More Tools

An extensive list of text mining applications is maintained inhttp://zope.bioinfo.cnio.es/bionlp_tools​/ [62]. A growing number of tools are being developed under a standard framework called UIMA, which comprises NLP as well as BioNLP tools [63].

Conclusion

Text mining tools are increasingly more accessible to biologists and computational biologists and these can often be applied to answer scientific questions in combination with other bioinformatics tools. Getting acquainted with them is a first step towards grasping the possibilities of text mining and towards venturing into the algorithms described in the literature. One way to get started on this path is by looking at examples such as [64][67].

Acknowledgments

I would like to thank Rohitha P. SriRamaratnam for comments on the manuscript.

References

  1. 1.Sobell JM, Weinberg JM (2009) Patient fatalities potentially associated with efalizumab use. J Drugs Dermatol 8: 215.
  2. 2.Cohen KB, Hunter L (2008) Getting started in text mining. PLoS Comput Biol 4: e20. doi:10.1371/journal.pcbi.0040020.
  3. 3.Rzhetsky A, Seringhaus M, Gerstein MB (2009) Getting started in text mining: part two. PLoS Comput Biol 5: e1000411. doi:10.1371/journal.pcbi.1000411.
  4. 4.Rzhetsky A, Seringhaus M, Gerstein M (2008) Seeking a new biology through text mining. Cell 134: 9–13.
  5. 5.Friedman C, Kra P, Rzhetsky A (2002) Two biomedical sublanguages: a description based on the theories of Zellig Harris. J Biomed Inform 35: 222–235.
  6. 6.Netzel R, Perez-Iratxeta C, Bork P, Andrade MA (2003) The way we write. EMBO Rep 4: 446–451.
  7. 7.Krauthammer M, Nenadic G (2004) Term identification in the biomedical literature. J Biomed Inform 37: 512–526.
  8. 8.Tanabe L, Xie N, Thom LH, Matten W, Wilbur WJ (2005) GENETAG: a tagged corpus for gene/protein named entity recognition. BMC Bioinformatics 6: Suppl 1S3.
  9. 9.Kabiljo R, Shepherd AJ (2008) Protein name tagging in the immunological domain. Proceedings of the Third International Symposium on Semantic Mining in Biomedicine (SMBM 2008) 141–144.
  10. 10.Lu X, Zhai C, Gopalakrishnan V, Buchanan BG (2004) Automatic annotation of protein motif function with Gene Ontology terms. BMC Bioinformatics 5: 122.
  11. 11.Morgan AA, Lu Z, Wang X, Cohen AM, Fluck J, et al. (2008) Overview of BioCreative II gene normalization. Genome Biol 9: Suppl 2S3.
  12. 12.Liu H, Hu ZZ, Zhang J, Wu C (2006) BioThesaurus: a web-based thesaurus of protein and gene names. Bioinformatics 22: 103–105. Available:http://pir.georgetown.edu/pirwww/iprolin​k/biothesaurus.shtml.
  13. 13.Bangalore A, Thorn KE, Tilley C, Peters L (2003) The UMLS knowledge source server: an object model for delivering UMLS data. AMIA Annu Symp Proc 51–55. Available:http://www.nlm.nih.gov/research/umls/.
  14. 14.Aronson AR (2001) Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. Proc AMIA Symp 17–21. Available: http://mmtx.nlm.nih.gov/.
  15. 15.Rebholz-Schuhmann D, Arregui M, Gaudan S, Kirsch H, Jimeno A (2008) Text processing through web services: calling Whatizit. Bioinformatics 24: 296–298. Available: http://www.ebi.ac.uk/webservices/whatizi​t/info.jsf.
  16. 16.Settles B (2005) ABNER: an open source tool for automatically tagging genes, proteins and other entity names in text. Bioinformatics 21: 3191–3192. Available:http://pages.cs.wisc.edu/~ bsettles/abner/.
  17. 17.Shah PK, Bork P (2006) LSAT: learning about alternative transcripts in MEDLINE. Bioinformatics 22: 857–865. Available: http://www.bork.embl.de/LSAT.
  18. 18.Shtatland T, Guettler D, Kossodo M, Pivovarov M, Weissleder R (2007) PepBank–a database of peptides based on sequence text mining and public peptide data sources. BMC Bioinformatics 8: 280. Available: http://pepbank.mgh.harvard.edu/.
  19. 19.Wermter J, Tomanek K, Hahn U (2009) High-performance gene name normalization with GeNo. Bioinformatics 25: 815–821. Available: http://www.semedico.org/.
  20. 20.Alonso-Allende R (2009) Accelerating searches of research grants and scientific literature with novo|seek. Nat Methods 6. Advertising feature. Available:http://www.novoseek.com/.
  21. 21.Doms A, Schroeder M (2005) GoPubMed: exploring PubMed with the Gene Ontology. Nucleic Acids Res 33: W783–W786. Available: http://www.gopubmed.com.
  22. 22.Plake C, Royer L, Winnenburg R, Hakenberg J, Schroeder M (2009) GoGene: gene annotation in the fast lane. Nucleic Acids Res 37(Web Server issue) W300–W304. Available: http://www.gopubmed.org/gogene/.
  23. 23.Shatkay H, Pan F, Rzhetsky A, Wilbur WJ (2008) Multi-dimensional classification of biomedical text: toward automated, practical provision of high-utility text to diverse users. Bioinformatics 24: 2086–2093.
  24. 24.Viallard JF, Lazaro E, Ellie E, Eimer S, Camou F, et al. (2007) Improvement of progressive multifocal leukoencephalopathy after cidofovir therapy in a patient with a destructive polyarthritis. Infection 35: 33–36.
  25. 25.Smith L, Rindflesch T, Wilbur WJ (2004) MedPost: a part-of-speech tagger for bioMedical text. Bioinformatics 20: 2320–2321. Available:http://www.ncbi.nlm.nih.gov/staff/lsmith​/MedPost.html.
  26. 26.Santos C, Eggle D, States DJ (2005) Wnt pathway curation using automated natural language processing: combining statistical methods with partial and full parse for knowledge extraction. Bioinformatics 21: 1653–1658.
  27. 27.Friedman C, Kra P, Yu H, Krauthammer M, Rzhetsky A (2001) GENIES: a natural-language processing system for the extraction of molecular pathways from journal articles. Bioinformatics 17: Suppl 1S74–S82.
  28. 28.Blaschke C, Valencia A (2001) The potential use of SUISEKI as a protein interaction discovery tool. Genome Inform 12: 123–134.
  29. 29.Hu ZZ, Narayanaswamy M, Ravikumar KE, Vijay-Shanker K, Wu CH (2005) Literature mining and database annotation of protein phosphorylation using a rule-based system. Bioinformatics 21: 2759–2765.
  30. 30.Yuan X, Hu ZZ, Wu HT, Torii M, Narayanaswamy M, et al. (2006) An online literature mining tool for protein phosphorylation. Bioinformatics 22: 1668–1669. Available:http://pir.georgetown.edu/pirwww/iprolin​k/rlimsp.shtml.
  31. 31.Tsuruoka Y, Tsujii J, Ananiadou S (2008) FACTA: a text search engine for finding associated biomedical concepts. Bioinformatics 24: 2559–2560. Available:http://text0.mib.man.ac.uk/software/fact​a/.
  32. 32.Hu Y, Hines LM, Weng H, Zuo D, Rivera M, et al. (2003) Analysis of genomic and proteomic data using advanced literature mining. J Proteome Res 2: 405–412. Available: http://medgene.med.harvard.edu/MEDGENE/.
  33. 33.Rolfs A, Hu Y, Ebert L, Hoffmann D, Zuo D, et al. (2008) A biomedically enriched collection of 7000 human ORF clones. PLoS ONE 3: e1528. Available:http://biogene.med.harvard.edu/BIOGENE/.
  34. 34.Aerts S, Lambrechts D, Maity S, Van Loo P, Coessens B, et al. (2006) Gene prioritization through genomic data fusion. Nat Biotechnol 24: 537–544. Available:http://homes.esat.kuleuven.be/~ bioiuser/endeavour/endeavour.php.
  35. 35.Perez-Iratxeta C, Wjst M, Bork P, Andrade MA (2005) G2D: a tool for mining genes associated with disease. BMC Genet 6: 45.
  36. 36.Cheng D, Knox C, Young N, Stothard P, Damaraju S, et al. (2008) PolySearch: a web-based text mining system for extracting relationships between human diseases, genes, mutations, drugs and metabolites. Nucleic Acids Res 36: W399–W405. Available:http://wishart.biology.ualberta.ca/polys​earch/index.htm.
  37. 37.Jelier R, Schuemie MJ, Veldhoven A, Dorssers LC, Jenster G, et al. (2008) Anni 2.0: a multipurpose text-mining tool for the life sciences. Genome Biol 9: R96. Available:http://www.biosemantics.org/index.php?pa​ge=anni-2-0.
  38. 38.Hoffmann R, Valencia A (2004) A gene network for navigating the literature. Nat Genet 36: 664. Available: http://www.ihop-net.org/.
  39. 39.Lee H, Yi GS, Park JC (2008) E3Miner: a text mining tool for ubiquitin-protein ligases. Nucleic Acids Res 36: W416–W422. Available: http://e3miner.biopathway.org.
  40. 40.Swanson DR (1988) Migraine and magnesium: eleven neglected connections. Perspect Biol Med 31: 526–557.
  41. 41.Weeber M, Kors JA, Mons B (2005) Online tools to support literature-based discovery in the life sciences. Brief Bioinform 6: 277–286.
  42. 42.Smalheiser NR, Torvik VI, Zhou W (2009) Arrowsmith two-node search interface: a tutorial on finding meaningful links between two disparate sets of articles in MEDLINE. Comput Meth Program Biomed 94: 190–197. Available:http://arrowsmith.psych.uic.edu/cgi-bin/​arrowsmith_uic/start.cgi.
  43. 43.Caporaso JG, Deshpande N, Fink JL, Bourne PE, Cohen KB, et al. (2008) Intrinsic evaluation of text mining tools may not predict performance on realistic tasks. Pac Symp Biocomput 640–651.
  44. 44.Zanzoni A, Montecchi-Palazzi L, Quondam M, Ausiello G, Helmer-Citterich M, et al. (2002) MINT: a Molecular INTeraction database. FEBS Lett 513: 135–140.
  45. 45.Marcotte EM, Xenarios I, Eisenberg D (2001) Mining literature for protein-protein interactions. Bioinformatics 17: 359–363.
  46. 46.Donaldson I, Martin J, de Bruijn B, Wolting C, Lay V, et al. (2003) PreBIND and Textomy–mining the biomedical literature for protein-protein interactions using a support vector machine. BMC Bioinformatics 4: 11.
  47. 47.Rodriguez-Esteban R, Iossifov I, Rzhetsky A (2006) Imitating manual curation of text-mined facts in biomedicine. PLoS Comput Biol 2: e118. doi:10.1371/journal.pcbi.0020118.
  48. 48.Wadman M (2009) Open-access policy flourishes at NIH. Nature 458: 690–691.
  49. 49.Vastag B (2000) NIH launches PubMed Central. J Natl Cancer Inst 92: 374. Available:http://www.ncbi.nlm.nih.gov/pmc/.
  50. 50.Hearst MA, Divoli A, Guturu H, Ksikes A, Nakov P, et al. (2007) BioText Search Engine: beyond abstract search. Bioinformatics 23: 2196–2197. Available:http://biosearch.berkeley.edu/.
  51. 51.Xu S, McCusker J, Krauthammer M (2008) Yale Image Finder (YIF): a new search engine for retrieving biomedical images. Bioinformatics 24: 1968–1970. Available:http://krauthammerlab.med.yale.edu/image​finder/.
  52. 52.Rodriguez-Esteban R, Iossifov I (2009) Figure mining for biomedical research. Bioinformatics 25: 2082–2084.
  53. 53.Yandell MD, Majoros WH (2002) Genomics and natural language processing. Nat Rev Genet 3: 601–610.
  54. 54.Koussounadis A, Redfern OC, Jones DT (2009) Improving classification in protein structure databases using text mining. BMC Bioinformatics 10: 129.
  55. 55.Pandev G, Kumar V, Steinbach M (2006) Computational approaches for protein function prediction: a survey. Technical Report 06-028, Department of Computer Science and Engineering, University of Minnesota, Twin Cities.
  56. 56.Manning CD, Schutze H (1999) Foundations of Statistical Natural Language Processing. MIT Press.
  57. 57.Van Rijsbergen CJ, Robertson SE, Porter MF (1980) New models in probabilistic information retrieval. Tech. Rep. 5587. British Library. Available: http://tartarus.org/~ martin/PorterStemmer/.
  58. 58.Krallinger M, Valencia A (2005) Text-mining and information-retrieval services for molecular biology. Genome Biol 6: 224.
  59. 59.Shatkay H, Höglund A, Brady S, Blum T, Dönnes P, et al. (2007) SherLoc: high-accuracy prediction of protein subcellular localization by integrating text and protein sequence data. Bioinformatics 23: 1410–1417. Available: http://www-bs.informatik.uni-tuebingen.d​e/Services/SherLoc2/.
  60. 60.Brady S, Shatkay H (2008) EpiLoc: a (working) text-based system for predicting protein subcellular location. Pac Symp Biocomput 604–615. Available:http://epiloc.cs.queensu.ca/.
  61. 61.Glenisson P, Coessens B, Van Vooren S, Mathys J, Moreau Y, et al. (2004) TXTGate: profiling gene groups with text-based information. Genome Biol 5: R43. Available:http://tomcat.esat.kuleuven.be/txtgate/.
  62. 62.Krallinger M, Hirschman L, Valencia A (2008) Linking genes to literature: text mining, information extraction, and retrieval applications for biology. Genome Biol 9: S8. Available: http://zope.bioinfo.cnio.es/bionlp_tools​/.
  63. 63.Kano Y, Baumgartner WA Jr, McCrohon L, Ananiadou S, Cohen KB, et al. (2009) U-Compare: share and compare text mining tools with UIMA. Bioinformatics 25: 1997–1998. Available: http://u-compare.org/.
  64. 64.Ramialison M, Bajoghli B, Aghaallaei N, Ettwiller L, Gaudan S, et al. (2008) Rapid identification of PAX2/5/8 direct downstream targets in the otic vesicle by combinatorial use of bioinformatics tools. Genome Biol 9: R145.
  65. 65.Natarajan J, Berrar D, Dubitzky W, Hack C, Zhang Y, et al. (2006) Text mining of full-text journal articles combined with gene expression analysis reveals a relationship between sphingosine-1-phosphate and invasiveness of a glioblastoma cell line. BMC Bioinformatics 7: 373.
  66. 66.Leach SM, Tipney H, Feng W, Baumgartner WA, Kasliwal P, et al. (2009) Biomedical discovery acceleration, with applications to craniofacial development. PLoS Comput Biol 5: e1000215. doi:10.1371/journal.pcbi.1000215.
  67. 67.Campillos M, Kuhn M, Gavin AC, Jensen LJ, Bork P (2008) Drug target identification using side-effect similarity. Science 321: 263–266.
  68. 68.Leitner F, Krallinger M, Rodriguez-Penagos C, Hakenberg J, Plake C, et al. (2008) Introducing meta-services for biomedical information extraction. Genome Biol 9: Suppl 2S6. Available: http://bcms.bioinfo.cnio.es/.
  69. 69.Fernández JM, Hoffmann R, Valencia A (2007) iHOP web services. Nucleic Acids Res 35(Web Server issue) W21–W26.
  70. 70.Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, et al. (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Research 13: 2498–2504. Available: http://www.cytoscape.org/.
  71. 71.Wilbur WJ, Rzhetsky A, Shatkay H (2006) New directions in biomedical text annotation: definitions, guidelines and corpus construction. BMC Bioinformatics 7: 356.
  72. 72.Rzhetsky A, Zheng T, Weinreb C (2006) Self-correcting maps of molecular pathways. PLoS One 1: e61. doi:10.1371/journal.pone.0000061.
  73. 73.Jenssen TK, Laegreid A, Komorowski J, Hovig E (2001) A literature network of human genes for high-throughput analysis of gene expression. Nat Genet 28: 21–28.

text-mining tools and resources

TABLE 1 | Examples of text-mining tools and resources

FROM THE FOLLOWING ARTICLE:

Text-mining solutions for biomedical research: enabling integrative biology

Dietrich Rebholz-Schuhmann, Anika Oellrich & Robert Hoehndorf

Nature Reviews Genetics 13, 829-839 (December 2012)

doi:10.1038/nrg3337

Name Content Input Description URL
Information retrieval
PubMed Abstracts Standard query Retrieves abstracts of scientific publications according to user query. Results are provided as a list and can be further filtered with Medical Subject Headings (MeSH) terms and an advanced search functionality http://www.ncbi.nlm.nih.gov/pubmed
GoPubMed Abstracts Standard query Retrieves publications from MEDLINE and additional functionality by classifying publications according to Gene Ontology concepts to allow improved screening of results http://www.gopubmed.com/web/gopubmed
RefMED Any text Standard query Allows user to submit feedback and consequently learns how to search PubMed for relevant articles according to feedback provided http://dm.postech.ac.kr/refmed
UK PubMed Central (UKPMC) Full text Standard query Retrieves full-text documents from PubMed and mines the documents for mentions of genes, drugs and Gene Ontology concepts using the Whatizit infrastructure http://ukpmc.ac.uk
PolySearch Abstracts, databases Standard query Retrieves information (such as documents and database entries) according to particular patterns of queries. Supports 50 different classes of queries http://wishart.biology.ualberta.ca/polysearch/index.htm
Information extraction
Textpresso Full text Standard query Provides extracted statements containing entities of interest on a subset of full text articles. A subset of articles is determined by Textpresso itself: for example, only mouse- or worm-specific articles http://www.textpresso.org
CoPub Abstracts Concepts or identifiers Retrieves co-occurring biomedical concepts from MEDLINE abstracts. The user specifies a list of concepts or identification numbers and retrieves back an overview about co-occurring concepts divided into categories http://services.nbic.nl/copub/portal
iHOP Abstracts Standard query Processes MEDLINE abstracts and generates a hyperlinked set of data for protein interactions. iHOP provides interactive functionality for searching genes and related information http://www.ihop-net.org/UniPub/iHOP
Reflect Any text Proteins Processes documents to highlight proteins and small molecules in the document and to link the entity to reference data resources http://reflect.embl.de
Open Biomedical Annotator Any text Ontologies and configuration parameters Processes documents to annotate text spans with ontology concepts. Covers all ontologies provided from the BioPortal Web page http://bioportal.bioontology.org/annotator
Database
Side Effect Resource (SIDER) Holds information about the side effects of drugs extracted from drug leaflets and scientific literature http://sideeffects.embl.de
PharmGKB Provides information about the influences of genetic variation on drug responses. Information is extracted from scientific literature and is partially curated http://www.pharmgkb.org
BioCaster Retrieves disease relevant information from Twitter tweets and shows current hotspots of disease outbreaks on an interactive map http://born.nii.ac.jp
Transcript Based Isoform Interaction Database (TBIID) Provides information on human protein isoforms and their differential interactions http://tbiid.emu.edu.tr
STITCH Holds known and predicted interactions of small molecules and proteins, partially derived from scientific literature http://stitch.embl.de
The table gives an overview of data resources and tools that are available to the public. For each category, a selection has been chosen to demonstrate the purpose of that category.

Pathway and Network Approaches for Identification of Cancer Signature Markers from Omics Data

J Cancer 2015; 6(1):54-65. doi:10.7150/jca.10631

Review

Pathway and Network Approaches for Identification of Cancer Signature Markers from Omics Data

Jinlian Wang1,7, Yiming Zuo1,6, Yan-gao Man2, Itzhak Avital2, Alexander Stojadinovic2,3, Meng Liu4, Xiaowei Yang4, Rency S. Varghese1, Mahlet G Tadesse5, Habtom W Ressom1 Corresponding address

1. Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA;
2. Bon Secours Cancer Institute, Richmond VA, USA;
3. Division of Surgical Oncology, Walter Reed National Military Medical Center, Bethesda, MD, USA;
4. Department of Public Health School of Hunter College, City University of New York, NYC, USA;
5. Department of Mathematics and Statistics, Georgetown University, Washington DC, USA;
6. Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA;
7. Genetics and Genomics Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

How to cite this article:
Wang J, Zuo Y, Man Yg, Avital I, Stojadinovic A, Liu M, Yang X, Varghese RS, Tadesse MG, Ressom HW. Pathway and Network Approaches for Identification of Cancer Signature Markers from Omics Data. J Cancer 2015; 6(1):54-65. doi:10.7150/jca.10631. Available from http://www.jcancer.org/v06p0054.htm

Abstract

The advancement of high throughput omic technologies during the past few years has made it possible to perform many complex assays in a much shorter time than the traditional approaches. The rapid accumulation and wide availability of omic data generated by these technologies offer great opportunities to unravel disease mechanisms, but also presents significant challenges to extract knowledge from such massive data and to evaluate the findings. To address these challenges, a number of pathway and network based approaches have been introduced. This review article evaluates these methods and discusses their application in cancer biomarker discovery using hepatocellular carcinoma (HCC) as an example.

Keywords: Biological pathways, system biology, high-throughput omics data, cancer biomarker.

Introduction

A better understanding of disease associated with biomarkers could potentially start a new area for uncovering the mechanism of cancer progression, development and offer better targets for drug development [1]. Studies on single gene/protein/metabolite molecular signatures offer limited insight into the complex interplay among the molecules responsible for progression of complex diseases such as cancer. Thus, there is a shift toward the identification of a panel of genes that interact directly or indirectly in the form of pathway or complex network to evaluate their association to cancer [2,3]. This is accomplished through massive data derived by high throughput omic technologies such as next generation sequencing, microarray, and mass spectrometry. Although thousands of candidate biomarkers have been discovered by these technologies, few of them have been transferred into practical application in clinical setting and new drug production. The challenges lie in (1) high false positive rate of the candidate biomarkers identified from omics data; (2) Lack of attention on the study of the context of biomarkers who are interacting each other in the form of pathway or network associated with cancer; (3) Fragmental and incomplete information based on biomarkers identified from solely omics platform; (4) Lack of effective algorithms that allow integration of diverse omics data sources to simulate the biological pathway and networks. To meet these challenges, a number of pathway and network based approaches have been introduced. This review article evaluates the advantages and limitations of these methods.

The traditional approaches that individual and a panel of cancer biomarkers are selected by analytic methods such as analysis of variance (ANOVA), Lasso, pairwise, information theory and support vector machine (SVM) do not explicitly consider interaction between genes, proteins and metabolites. Compared to traditional methods, pathway and network centric methods naturally provide a way to understand the underlying pathways and the interactions between individual signature markers and non-markers. With the large-scale generation and integration of genomic, transcriptomic, proteomic, and metabolomic data, pathway/network-based methods provide a more effective and accurate means for cancer biomarker discovery. Increasingly, pathway and network-based analyses are applied to omics data to gain more insight into the underlying biological function and processes, such as cell signaling and metabolic pathways as well as gene regulatory networks [4-6]. A number of pathway /network approaches have also been used for improving the prediction of cancer outcome, providing novel hypotheses for pathways involved tumor progression [7], and exploring cancer associated biomarkers [8]. For example, Taylor et al. [9] combined gene expression data with physical protein-protein interaction data to identify subnetwork markers for the prognosis of breast cancer and lymphoma patients. Torkamani and Schork [10] used gene co-expression network to infer cancer-initiating genes in breast, colorectal cancer, and glioblastoma. Kim et al. applied the MAPIT (Multi Analyte Pathway Inference Tool) algorithm to identify prognostic network markers to predict GBM patient survival time using multi-analyte network markers discovered by integrating gene expression profile, epigenomic profile, and protein-protein interactome [11]. Goh et al. [12] built a human disease network (HDN) by linking hereditary disease that share a disease-causing gene recorded in Mendelian Inheritance in Man (OMIM) database. Although the functional connections in the HDN remain to be further demonstrated, it inspires us to systematically study the relationships among diseases by constructing a network. More detailed descriptions of relationships between human disease and network essential for understanding of human have been recently summarized in reviews [2, 12-14].

In this review, we provide some pathway and network centric computational approaches and their applications for biomarker discovery.

Summary of pathways and networks centric approaches for cancer biomarker discovery

Availability of biomedical pathways and networks based on large-scale data gathering through diverse omics data sources offers new opportunities to explain the causality of relationships between biological entities and cancers [15]. As shown in Figure 1, the general steps of the biomarker discovery include the following: 1) Define precisely a well-framed, relevant clinical problem and focus the experimental design around appropriate study populations and samples; 2) Collect tissue samples or fluids from patients and suitable assays; 3) Acquire high-throughput data from the omics technologies; 4) Analyze the data using signal processing, statistical and machine learning methods to select relevant features from the data; 5) Integrate the pathway/network knowledge from databases such as KEGG, HMDB and Reatcome mapping candidate biomarkers to the corresponding pathways or networks; 6) Evaluate biomarkers to estimate their diagnostic or prognostic capability and clinical validity using alternative technologies such as Westen blot, ELSA, and RT-PCR. In computational aspect, cross-validation and independent validation are the commonly used methods to evaluate the performance of a biomarkers. P-values, sensitivity, specificity and the area under receiver operating curves (AUC) are used as quantitative indicators of the performance of the methods [16]; 7) Use the biomarkers for clinical applications after reliable pre-clinical tests and validation of the markers in a large population.

Statistics methods

Statistical methods test scientific theories when observations, processes or boundary conditions are subject to stochasticity. For examples, the classical t-test has been extensively used for testing differential gene expression in microarray data [35]. However, this kind of procedure relies on reasonable estimates of reproducibility or within-gene error, requiring a large number of replicated arrays. Thus, several methods for improving estimates of variability and statistical tests of differential expression have been proposed. For example, Significance Analysis of Microarrays (SAM) aimed to improve the unstable error estimation in the two-sample t-test by adding a variance stabilization factor which minimizes the variance variability across different intensity ranges [36, 37]. ANOVA model approach is widely used in multiple kinds of omic data. For example, it was used to model microarray data with the effects of array, condition, and condition-array interaction and then to fit the residuals with the effects of gene, gene-condition interaction, and gene-array interaction [38,39]. Also, it was applied to capture the effects of controlled groups, batches, condition, alias of experimental equipment, and condition-metabolite interaction separately on LC-MS data [40]. To improve the accuracy and sensitivity of analytic results, false discovery rate (FDR) [41] and its refinement, q-value, (q-value package,www.bioconductor.org) have been rapidly adopted for genomic, proteomics and metabolic data analysis including the widely-used SAM, DAVID [42] and other approaches [36]. Another statistics method for biomarker discovery is linear discriminant analysis (LDA), one of the classical statistical classification techniques based on the multivariate normal distribution assumption, is quite robust and powerful to discover biomarker or pathways between omics data for many different applications despite the distributional assumption. Compared to LDA, quadratic discriminant analysis (QDA) requires more observations to estimate each variance-covariance matrix for each class [43]. In addition, logistic regression analysis has been successfully used to evaluate biomarker performance of prostate cancer with mRNA profiling [44]. Logistic regression (LR) model based on the regression fit on probabilistic odds between comparing conditions requires no specific distribution assumption (e.g. Gaussian distribution) but is often found to be less sensitive than other approaches[42,43].

Graph theory based network and visualization

The modeling fundamentals of graph theory are often used to describe the global topology, structure or the community of a complex system. It emphasizes on entities (e.g, genes, proteins, diseases, biological process) and the relations between them. The complexity of graphical modeling can be either simple only with nodes and edges or more complex where edges have weights, and nodes and edges can be of different types. Recent publications have applied graphical modeling in computational biology to study biological networks, enhance the ability to draw causal inferences from functional MRI experiments, support the early detection of disconnection and the modeling of pathology spread in neurodegenerative disease such as Alzheimer’s disease [45-49]. For example, in mammalian cells, Bleris et al. have had early success in characterizing the dynamics of key feed forward modules and motifs, helping to enable the circuit design of adaptive gene expression [50]. Using graph based approaches, Ma’ayan et al. model cellular machinery including genes, proteins and other subcellular compartments [51], in which the interactions between components are drawn as edge connections between the relevant nodes [51]. Gene expression data combined with network analysis can yield important information on how expression variation relates to differences between observed states [52]. As closely connected genes tend to be involved in similar functions, network annotation can complement clusters obtained via fold change analysis [7]. A standard systems-based approach to biomarker and drug target discovery consists of placing putative or known biomarkers in the context of a network of biological interactions, followed by different ‘guilt-by-association’ analyses [53].

 Figure 1The pipeline of pathway/networks centric approach for cancer biomarker discovery. A variety of computational tools and algorithms have been proposed for biomarker discovery based on pathway and network methods. The most commonly used methods are categorized roughly into statistical [17], graph theory [18], Bayesian methods [19], text mining [20], machine learning [21-23] and integrative methods summarized in Table 1.J Cancer Image (Click on the image to enlarge.)

 Table 1Computational methods for biomarker discovery categorized by their application, examplary tools and URLs.

Approaches Technique & Application Examples Exemplary Tools &URL
Statistical analysis Hypothesis testing, random sampling. ANOVA. Detection of differentially expressed genes/proteins, genotypes, biomarker filtering/selection[24] BRB:http://linus.nci.nih.gov/BRB-ArrayTools.html
PAM: http://www-stat.stanford.edu/~tibs/PAM/
SAM: http://www-stat.stanford.edu/~tibs/SAM/
Pattern recognition Machine learning, Probabilistic, instance-based, kernel classification models. Clustering, multi-source data classification, biomarker selection and associations [25]
Bayesian regression models [26], partial least squares [27], and Genetic Algorithm/KNN [28].
Weka: http://weka.wikispaces.com/
LIBSVM: http://www.csie.ntu.edu.tw/~cjlin/libsvm/
PRTools: http://prtools.org/
R package: http://cran.r-project.org/web/views/Bayesian.html
Graph/network theory Network topology analysis, network visualization and data integration, clustering. Genetic, regulatory, protein-protein, signaling network analysis, biomarker/target identification
[29]
BioNet[4] :http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm285284.htm
Jung: http://jung.sourceforge.net/
http://bioinfo.mc.vanderbilt.edu/dmGWAS.html [30]
Data visualization and imaging Sequence and cluster visualization, interactive visualization, statistical analysis graphs. Data exploration, biomarker visualization, model explanation, in vivo/in vitro imaging of molecules and cells [29] Cytoscape [31]: http://www.biotapestry.org/
Medusa: http://coot.embl.de/medusa/
Graphviz: http://www.graphviz.org/
Osprey:http://biodata.mshri.on.ca/osprey/servlet/Index
Pajek: http://vlado.fmf.uni-lj.si/pub/networks/pajek/
3Omics: http://3omics.cmdm.tw
Natural language processing and information retrieval Ontologies, text mining, information representation standards, information retrieval and extraction. Inference of functional associations from publications, automated annotation and characterization [32,33] iHOP: http://www.ihop-net.org/UniPub/iHOP
CoPub: http://services.nbic.nl/copub/portal
PolySearch: http://wishart.biology.ualberta.ca/polysearch/
index.htm
Open Biomedical Annotator: http://bioportal.bioontology. org/annotator
GeneSeeker: http://www.cmbi.ru.nl/GeneSeeker/
Software development, Internet technologies Data warehouses and distributed information systems, semantic Web tools, information retrieval, extraction and curation. Biomarker discovery and validation platforms, data mining tools, search and reasoning engines [34] IPA:http://www.ingenuity.com/products/pathways_analysis.htm
GO: http://www.geneontology.org/GO.tools.shtml
MiMI: http://mimi.ncibi.org/MimiWeb/main-page.jsp

The goal of visualization is to find patterns and structures that remain hidden in the raw unstructured datasets. Graph visualization is key to display directly the various relationships between entities (e.g., genes, proteins). Challenges of graph visualization lie in 1) the high false positive rate of incorporating heterogeneous multi-omic datasets; 2) Visual representation of the logical structure transformed from the raw data; 3) Graph manipulation and layout algorithm for representing the complicated relationships between biological entities. 4) Heterogeneous omic data from different level visualization needs more flexibility for layered representation. A number of commercial and free sourced graph visualization tools and platforms have been extensively developed. For example, Cytoscape [31], one of the free open source platforms providing biological network analysis and visualization with more than 172 registered plugins contributed by the community, is very versatile in network applications, such as network importing, network integrating, inference customization, literature mining, topological clustering, functional enrichment, network comparison, and programmatic access [54]. 3DScapeCS, a Cytoscape plugin providing three-dimensional, dynamic, parallel network visualization for Mass Spectrometry (MS) molecular network [55]. IPA [56], a commercial software tool for pathway analysis with omics data provides powerful graphical visualized pathways and networks overlaid by diseases, drugs and biological process etc. PathwayStudio provide abstractive graphical interface for users to analyze gene expression, protein interaction and metabolic data to analyze and explore the pathways and networks identified from data. STRING not only gives the graphical visualized protein interaction of both known and predicted but also quantifies each pair of proteins by their interaction types such as physical interaction and gene fusion etc. [57].

Bayesian methods and its derivatives

Bayesian methods allow informative priors so that prior knowledge or results of a previous model can be used to inform the current model. In cancer bioinformatics and systems biology, the primary application of Bayesian methods include Bayesian inference, Bayesian network, Naive Bayes classifier and Bayesian variable selection. Among these methods, Bayesian network is one of the most common modeling tools for pathway and network analysis [19]. Bayesian network is a form of directed statistical modeling designed to capture conditional dependencies between probabilistic events [58]. It consists of a dependency structure and local probability model also named probabilistic graph models which include Hierarchical Bayesian Networks (HBN), Probabilistic Boolean Networks (PBN), Hidden Markov Models (HMM), and Markov Logic Networks (MLN) [59-61]. The dependency structure specifies how the variables are related to each other by drawing directed edges between the variables without creating directed cycles. Each variable depends on a possibly set of other variables, termed “parents.” Compared with other pathway/network centric method, Bayesian network model is capable of integrating heterogeneous data, missing value and dependent relationships between variables [62].

In a Bayesian network model, probabilities define the relationship between the current node and its predecessor or parent in a graph [63]. The power of these methods lies in their ability to facilitate the reverse engineering of multiplex networks based on molecular expression, molecular activity and/or cell behavior data, serving as a precursor to synthetic modifications of existing molecular pathways [64]. Bayesian inference is one of the very important Bayesian methods widely used in cancer biomarker discovery, signaling pathway and network inference [65,66]. It has previously been applied to gene expression data for inference of gene regulatory networks [67,68], infer both protein signaling networks [69,70] and gene regulatory networks [71]. To incorporate an explicit time element, dynamic Bayesian Inference was proposed to interrogate dynamic signaling responses within a Bayesian framework, with existing signaling biology incorporated through an informative prior distribution on networks [66]. In addition, Bayesian variable selection aims at solving the problems of “large p, small n” existing in omic data set and using prior knowledge such as pathway and protein interaction to estimate the posterior probability by Markov Chain Monte Carlo (MCMC) also widely used to infer functional interactions in biochemical pathway, model the interactions between different functional modules of a biological network [72] and pathway based cancer biomarker discovery [73,74]. For example, Yang et al. [21] used a Bayesian network to construct HCC cell networks and identify functional modules and interactions between these modules. Stochastic simulation models offer an alternative, but they are hitherto associated with a major disadvantage: their likelihood functions cannot be calculated explicitly, and thus it is difficult to couple them to well-established statistical theory such as maximum likelihood and Bayesian statistics. A number of new methods, among them Approximate Bayesian Computing and Pattern-Oriented Modeling, bypass this limitation. The difference between Bayesian and frequentist inference lies in the following: 1) Bayesian inference provides answers conditional on the observed data and not based on the distribution of estimators or test statistics over imaginary samples not observed (Rossi et al., 2005, p. 4); 2) It includes uncertainty in the probability model, yielding more realistic predictions. 3) It safeguards against overfitting by integrating over model parameters. But the quality of the prior information directly impacts the performance of the Bayesian methods. Also, they are unable to account for feed- back regulation, a hallmark of signaling networks.

Text mining

With the growth of information in literature and biomedical databases, biological and clinical scientists need efficient means of handling and extracting diagnostic methods and prognostic terms and information from scientific literature. For this purpose, text mining that comprises the discovery and extraction of knowledge from free text to generate new hypotheses particularly relevant and helpful in biomedical research [14]. Text mining complements the reading of scientific literature by individual researchers, allows rapid access to information contained in large volume of documents and increases the reproducibility of literature searches by enabling users to process all documents for a specific result. The primary application of text-mining in biomedical research roughly lies in three aspects: 1) Simple text-mining such as transforming textual information into database content and integrating with existing knowledge resources to suggest novel hypotheses; 2) Literature analysis including clustering and classification of entities or diseases; 3) Integrative biology for producing or testing hypotheses against knowledge bases.

Currently, text mining is being successfully applied to the identification of molecular causes of diseases using facts from databases and literature [75-77]. For example, text-mining has been used to suggest disease biomarkers from the scientific literature, and made on the basis of the assumption that two proteins are likely to interact with each other if they share a substantial amount of contextual information [78,79]. By defining a gene of interest, a network is constructed from all scientific publications related to the query-defined gene. The results can be browsed by navigating through the visualized network. CoPub makes uses of lexical resources for genes, proteins, Gene Ontology labels, diseases, pathways, drugs and tissues to identify and statistically to qualify the significance of a specific term for a gene or a set of genes [80]. The results return a set of annotations for their genes of interest. Besides, text mining has been widely used in industrial large scale knowledge base for query genes, proteins, metabolic compounds and drugs functional analysis. To visualize knowledge contained in the scientific literature, software tools have been developed that provide improved integration of text-mining results with other data resources. For example, IPA (Ingenuity) [56], KEGG [81], Pathway Studio [82] and HPRD [83] use text-mining to integrate gene/protein-phenotype associations linking genes and protein variants to the diseases, toxic effects and drug response to their knowledge databases.

Depending on the tasks researchers address, text-mining can achieve different objectives. This include primarily the following: 1) retrieval information from relevant documents; 2) Identification of entities such as genes, diseases, complex relationship between entities and diseases and interactions between proteins and genes [80]; 3) Deposit extracted information into database or used to support manual database curation efforts [15]; 4) Generation hypothesis [79] and test novel research questions [78]. The trend of text-mining technique is shifting from the analysis of only abstracts to the full text of papers, from the analysis of gene and protein-related information to the information about cells, tissues and whole organisms. The most prominent shift is to integrate information from the literature with data sets from other domains such as gene expression profiles [84], genome-wide association studies (GWASs), biochemistry and phenotype [84,85]. Text-mining is prone to integration with machine learning, statistical techniques. In the future, text-mining might face several major challenges such as improve literature analysis, integrate to existing knowledge base, visualization of extracted information.

Machine learning

Machine learning methods have been used for the biomarker discovery from high-throughput omics data, inferring causal relations between mutations and diseases [21] , interactions between genes and proteins [86-88] and relations between environmental features and cancer [89] as well as pathway and network modeling. There are two kinds of basic machine learning techniques, one is unsupervised machine learning such as hierarchical clustering, self-organizing mapping (SOM) etc. [90]. The other is supervised machine learning which needs known knowledge from data train a model and then apply this model to predict the output variables [3]. A number of machine learning such as SVM [14], Artificial Neural Network [91], decision tree and random forests (RFs) etc. have been widely for various applications including identification of breast cancer biomarkers [92], diagnosis biomarker of Parkinson disorders [93], subcellular locations of proteins [94,95], the prediction of protein functions on the basis of protein structures [96,97], the annotation of mutations [98,99]. For example, Han proposed a machine learning based derivative component analysis method to select implicit feature by capturing subtle data behaviors and removing system noises from a proteomic profile to overcome the reproducibility problem for biomarker discovery in proteomics [100]. Another interesting study by Hoshida et al [101] combined eight independent cohorts of gene expression profiles to reveal the subclass of HCC and their related pathways using unsupervised machine learning methods. They found that three common subclasses (S1-S3) of hepatocellular carcinoma (HCC) were significantly correlated to Wnt pathway, MYC, AKT and hepatocyte differentiation respectively. Westen blotting; knockout and immunohistochemical staining were used for experimental validation of their discovery. Another framework called knowledge-driven matrix factorization (KMF) proposed by Yang et al. was used to reconstruct phenotype-specific modular gene networks [21].

Integrative methods

Integration of data from multiple omic studies not only can help unravel the underlying molecular mechanism of carcinogenesis but also identify the signature of signaling pathway/networks characteristic for specific cancer types that can be used for diagnosis, prognosis and guidance for targeted therapy. The methods described in Sections A-E have proven useful for discovering biomarkers from high-throughput omic data, analyzing protein-protein, protein-DNA, and kinase-substrate interactions, as well as for genetic interactions among genes [102]. These efforts have yielded good results in cancer biomarker discovery, protein interaction and interaction between genotype and diseases [103]. However, current omic technologies provide only limited fragmented reality of the biological functions within cell or cancer mechanism. Separate analysis of the data generated from each of these technologies is limited to revealing only partial aberrant molecular changes, because the interaction of multiple molecules cannot be modeled by isolated analysis of genes, proteins or metabolites. Furthermore, limitations such as intrinsic high noise, incomplete data, small sample-size, bias have motivated the use of integrative omic analysis and use of prior biological knowledge and information bases, rather than as mere collections of single large-scale omic studies [14, 34, 104]. However, integration of multiple disparate data types remains a significant challenge in systems biology research. Most recently, attempts at integration of multiple high-throughput omics data have concentrated on capturing regulatory associations between genes and proteins by comparing expression patterns across multiple conditions [105-107], combining functional characterization and quantitative evidence extracted from different data sources of all levels of gene products, mRNA, proteins and metabolites, as well as their interaction [108-110]. Some previous works [81, 111-113] in integrative analysis utilize pathways in the form of connected routes through a graph-based representation of the metabolic network [114]. Other approaches focus on the functional module of protein interaction network and analyze experimental data in the context of pathways using multiple source omics data [14,115,116]. We and others have developed advanced bioinformatics tools and algorithms to facilitate the integration of diverse data types [34, 110,117-120].

Different biological types of data, such as sequences, protein structures and families, proteomics data, ontologies, gene expression and other experimental data sets show a growing complexity produced by numerous heterogeneous application areas. The integration of heterogeneous data is therefore becoming more and more important. In order to gain insights into the complexity and dynamics of biological systems, the information stored in these data repositories needs to be linked and combined in efficient ways.

Application of biomarker discovery in HCC

Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the third leading cause of cancer death in the world, with the five-year survival rate approaching 7% [33]. Treatments of HCC include surgical resection and transplantation, ablation and transarterial chemoembolization, and systemic chemotherapy. Even so, no existing systemic chemotherapy is effective for advanced HCC [121,122]. For example, Lovet et al. [123] reported that targeted therapy with sorafenib which inhibits multiple tyrosine kinase receptors (RAS/VEGFR) may prolong survival by about three months. However, due to the redundancy and compensation of the signaling network in HCC, a significant reorganization of the signaling network observed such as down regulation of tumor suppressors (p53 and CHK1 when XIAP silenced or p-RB when CDK6 silenced) and upregulation of tumor promoting proteins (ETS1 when XIAP silenced or p-CREB when CDK6 silenced) may confer the growth benefit for cancer cells [124]. This example suggests providing pathways and network information may improve the efficacy of systemic chemotherapy of HCC. Chang et al [125] partitioned the complex oncogenic signaling networks into basic units, or functional modules, of signaling activity (e.g., a protein phosphorylating another protein to activate its kinase activity) and demonstrated that gene expression signatures based on these modules can predict the effectiveness of pathway-specific therapeutics [125]. Except for surgical resection/transplantation of early stage HCC, the survival time is not significantly prolonged by any of these treatments. Added to pathway and network centric method making use of omics data with systematic chemotherapy will benefit the development of newer therapeutic targets for HCC treatment.

In recent years, computational methods for models take more and more important roles in the HCC investigations [114,126,127]. Some computer systems have also been developed. For example, Shannon et al. [128] developed a java based tool Gaggle by integrating diverse databases (e.g., KEGG, BioCyc, String) and software (e.g., Cytoscape, R ) to simultaneously explore the experimental data (e.g., mRNA and protein abundance, protein-protein and protein-DNA interactions), functional associations, metabolic pathways (KEGG) and Pubmed abstracts. Recently, Zheng et al [129] identified the molecular events underlying the development of HCV induced HCC by integrating gene expression profile and protein interaction data. To get the subnetworks, they refined the network by removing a network component if the number of nodes is smaller than five. They found four subnetworks called normal-cirrhosis, cirrhosis-dysplasia, dysplasia-early and early-advanced HCC networks. From each of the sub networks they identified functional modules and hub genes. By comparing the pathways in each sub networks, they observed changes of pathways and network activities. Their findings were validated by literature. Even though the types of omics data they used only include gene expression and protein interactions, they provide a way to study the changes of network activities by analysis of omic data. Zhang et al. used systematical method including partial least squares, literature mining technique and with GeneGO Meta-Core to discover the biomarkers of HCC with gene expression as well as protein data. Based on these marker genes, they constructed down regulated and up regulated networks. In the former, they identified 10 up regulated hub genes (MAPK1, SP1, HDAC1, YY1, ABL1, PTK2, SMAD2, NCOA3; CDC25A and NCOA2). They identified 7 hub genes (FOS, ESR1, JUNB, EGFR, SOCS3; FOLH1 and IGF1) in the latter. Partial least squares were employed to construct a classifier with these biomarkers. They used five-fold cross-validation and two independent datasets to evaluate the performance of the classifier. Furthermore, they used experimental immunohistochemistry and western blot measurements to verify the marker genes predicted by the classifier. Their results show that the network-based approach facilitates biomarker identification and improves classification accuracy [130]. Hollywod et al [131] identified driver genes which are potent diagnosis markers and mechanism study of HCC using t-statistic map (TM) and transcriptome correlation map (TCM) approaches with integration of DNA copy number measured by genomics CGH array and gene expression. They found 50 driver genes with significant prognostic relevance to HCC key signaling pathways such as mTOR, AMPK, and EGFR. siRNA-mediated knockdown experiments was used to evaluate the functional significance of the 50 driver genes [131].

Even though collection of diverse omics data to analyze the relationships between HCC phenotype and biological entities within the cell has been proved powerful enough, such integration is still fragmentary, incomplete and inadequate to reflect the whole picture of the cancer information and development. The amount of omics data from genomics, proteomics, metabolomics and interactomics is increasing. In pace with the explosion of omics data, a number of open-access databases, containing comprehensive gene, protein interaction, biological pathway and network information, are being developed to provide biologists with valuable tools for analyzing the data from complex biological systems. These include IntAct, BioGRID, MINT, KEGG, PID, STRING and REACTOME etc. all of which provide very useful qualitative mappings of functional associations between key components in canonical pathways [14]. Table2 summarizes primary data source and URLs specific to HCC.

 Table 2Data sources and URLs for HCC databases.

Data sources URLs
EHCO[132] http://ehco.iis.sinica.edu.tw/
Onco.HCC[133] http://oncodb.hcc.ibms.sinica.edu.tw/index.htm
HCVpro[134] http://cbrc.kaust.edu.sa/hcvpro/
HCVdb[135] http://euhcvdb.ibcp.fr/euHCVdb/
Hepatitis Virus Database (HVDB) [136] http://s2as02.genes.nig.ac.jp
Los Alamos National Laboratory in the United States[137] http://hcv.lanl.gov
LiverAtlas[138] http://liveratlas.hupo.org.cn
dbHCCvar[139] http://GenetMed.fudan.edu.cn/dbHCCvar

Limitations of omics based biomarker discovery

With wide applications of omics technique, more accurate and ubiquitous biomarkers have been identified, but only few have been brought to clinical setting and many have proved to be irreproducible [140]. One of the concerns is that biomarkers identified suffer from low diagnostic specificity and sensitivity which leads to current cancer biomarkers have not yet made a major impact in reducing cancer burden. For instance, serum alpha-fetoprotein (AFP) is the most widely used biomarkers for detecting and monitoring of HCC, but the false negative rate with AFP levels may be high as 40% for patients with early stage of HCC, for advances patients, the AFP levels remain small in 15%-30% of patients [141].

One of the important limitations is possible artifacts in conducing biological experiments such as instrument variability. Others include bias in sample collection and sample handling which lead to cohort differences. For example, Sreekumar et al. [142] reported sarcosine as a prostate cancer biomarker through metabolomics analysis. However, subsequent validation study done by Jentzmik et al. [143] concluded that the levels of sarcosine measured by GC-MS could not differentiate malignant from nonmalignant tissue. Collestelli et al. reported no statistically significant difference between prostate cancer and healthy controls in the sarcosine to creatinine ratios and that the levels of sarcosine were about 11.7% higher in the healthy controls [144]. Another important limitation relates to lack of computational methods that can extract knowledge from omic data involving substantial amount of noise, high dimensionality, missing values, etc.

Although the use of pathway and network-based approaches and the integration of prior biological knowledge with omic data are promising in addressing some of the computational challenges, they too have some limitations as outlined below:

  • mRNA levels and DNA alterations may not accurately reflect the corresponding protein levels and fail to reveal changes in posttranscriptional protein modulation (e.g., phosphorylation, acetylation, methylation, ubiquitination, etc.) or protein degradation rates. Correlation of mRNA with its associated protein expression can be relatively low. The signaling network constructed using these approaches does not reflect the dynamic signal flow in a spatial relationship. Also, the genomic changes (mRNA level, SNP, CNV, methylation) ultimately affect protein expression, activation and inactivation, which, in turn, controls cellular behavior.
  • Current proteomic technologies provide only limited coverage of the proteome and more sensitive technologies are needed to identify and quantitate low abundant proteins [145,146].
  • Interpretation of pathway mapping results from the fact that pathway annotations currently take little consideration of tissue specificities of genes or proteins in the pathway. This limits the tissue and/or isoform specificity in pathway annotations. Thus, specific steps of a pathway may not be actually active in tissues/cells from which the omics data may be generated. In some cases, this may occur because protein isoforms or splice variants have been annotated as a protein class or a canonical protein sequence, respectively, in the pathway while they may be expressed differentially in different tissues/cells.
  • Because biological pathways are inherently complex and dynamic, pathway annotations in different pathway databases vary significantly in pathway models and in a number of other aspects, e.g., specific protein forms, dynamic complex formation, subcellular locations, and pathway cross talks.

Current computational methods thus need to provide a solution to these issues including revealing patterns within the data, modeling heterogeneity, profiling of disease classes and subclasses, producing a predictive of patients’ classification, etc.. Biomarker discovery is now changing research away from identification of individual biomarkers to searching for perturbed pathways and network activities.

Conclusion

Early detection of cancer improves survival and enhances quality of life. An ideal marker would be one that can be measured easily and reliably using an assay with high sensitivity and specificity and undergo rigorous validation before they are introduced into routine clinical care. Currently, the treatment of most cancers is based on the tissue types and clinical stages. This approach is often ineffective due to the heterogeneity of the tumors. Pathway and network based method have taken more important role in analysis of high-throughput data. Pathway and network based methods provide a global and systematical way to explore the relationships between biomarkers and their interacting partners. Thus, future work is likely to focus on using pathway and network based methods for biomarker discovery.

It is our expectation that methods discussed above will become a component in a shared infrastructure of biomedical resources that can be used by researchers to identify and to retrieve the most relevant work, to formulate hypothesis, to find supporting and contradicting evidence for hypotheses, to integrate research results into a framework of whole biological systems and to support the translation of research results across domains and into clinical applications.

Acknowledgements

This study was supported by NIH Grant R01CA143420 awarded to HWR.

Competing Interests

The authors have declared that no competing interest exists.

References

1. Li Y, Agarwal P. A pathway-based view of human diseases and disease relationships.PLoS One. 2009;4(2):e4346

2. Aitman TJ, Boone C, Churchill GA, Hengartner MO, Mackay TF, Stemple DL. The future of model organisms in human disease research. Nat Rev Genet. 2011;12(8):575-82

3. Baranzini SE. The genetics of autoimmune diseases: a networked perspective. Curr Opin Immunol. 2009;21(6):596-605

4. Ding Y, Chen M, Liu Z, Ding D, Ye Y, Zhang M, Kelly R, Guo L, Su Z, Harris SC, Qian F, Ge W, Fang H, Xu X, Tong W. atBioNet–an integrated network analysis tool for genomics and biomarker discovery. BMC Genomics. 2012;13:325

5. Feng M, Gao W, Wang R, Chen W, Man YG, Figg WD, Wang XW, Dimitrov DS, Ho M. Therapeutically targeting glypican-3 via a conformation-specific single-domain antibody in hepatocellular carcinoma. Proc Natl Acad Sci U S A. 2013;110(12):E1083-91

6. Murray RS. Myth of the chronic fatigue syndrome. West J Med. 1991;155(1):68

7. Chuang HY, Lee E, Liu YT, Lee D, Ideker T. Network-based classification of breast cancer metastasis. Mol Syst Biol. 2007;3:140

8. Dalerba P, Dylla SJ, Park IK, Liu R, Wang X, Cho RW, Hoey T, Gurney A, Huang EH, Simeone DM, Shelton AA, Parmiani G, Castelli C, Clarke MF. Phenotypic characterization of human colorectal cancer stem cells. Proc Natl Acad Sci U S A. 2007;104(24):10158-63

9. Taylor IW, Linding R, Warde-Farley D, Liu Y, Pesquita C, Faria D, Bull S, Pawson T, Morris Q, Wrana JL. Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat Biotechnol. 2009;27(2):199-204

10. Torkamani A, Schork NJ. Identification of rare cancer driver mutations by network reconstruction. Genome Res.2009;19(9):1570-8

11. Kim J, Gao L, Tan K. Multi-analyte network markers for tumor prognosis. PLoS One. 2012;7(12):e52973

12. Goh KI, Cusick ME, Valle D, Childs B, Vidal M, Barabasi AL. The human disease network. Proc Natl Acad Sci U S A.2007;104(21):8685-90

13. Loscalzo J, Barabasi AL. Systems biology and the future of medicine. Wiley Interdiscip Rev Syst Biol Med. 2011;3(6):619-27

14. Wang J, Zhang Y, Marian C, Ressom HW. Identification of aberrant pathways and network activities from high-throughput data. Brief Bioinform. 2012;13(4):406-19

15. Dowell KG, McAndrews-Hill MS, Hill DP, Drabkin HJ, Blake JA. Integrating text mining into the MGI biocuration workflow.Database (Oxford). 2009;2009:bap019

16. Ghosh D, Poisson LM. “Omics” data and levels of evidence for biomarker discovery. Genomics. 2009;93(1):13-6

17. Kuan PF, Wang S, Zhou X, Chu H. A statistical framework for Illumina DNA methylation arrays. Bioinformatics.2010;26(22):2849-55

18. Vinayagam A, Stelzl U, Foulle R, Plassmann S, Zenkner M, Timm J, Assmus HE, Andrade-Navarro MA, Wanker EE. A directed protein interaction network for investigating intracellular signal transduction. Sci Signal. 2011;4(189):rs8

19. Gevaert O, Van Vooren S, De Moor B. A framework for elucidating regulatory networks based on prior information and expression data. Ann N Y Acad Sci. 2007;1115:240-8

20. Kirouac DC, Saez-Rodriguez J, Swantek J, Burke JM, Lauffenburger DA, Sorger PK. Creating and analyzing pathway and protein interaction compendia for modelling signal transduction networks. BMC Syst Biol. 2012;6:29

21. Yamashita T, Ji J, Budhu A, Forgues M, Yang W, Wang HY, Jia H, Ye Q, Qin LX, Wauthier E, Reid LM, Minato H, Honda M, Kaneko S, Tang ZY, Wang XW. EpCAM-positive hepatocellular carcinoma cells are tumor-initiating cells with stem/progenitor cell features. Gastroenterology. 2009;136(3):1012-24

22. Mitsos A, Melas IN, Morris MK, Saez-Rodriguez J, Lauffenburger DA, Alexopoulos LG. Non Linear Programming (NLP) formulation for quantitative modeling of protein signal transduction pathways. PLoS One. 2012;7(11):e50085

23. Vineetha S, Chandra Shekara Bhat C, Idicula SM. MicroRNA-mRNA interaction network using TSK-type recurrent neural fuzzy network. Gene. 2013;515(2):385-90

24. Ji J, Ling J, Jiang H, Wen Q, Whitin JC, Tian L, Cohen HJ, Ling XB. Cloud-based solution to identify statistically significant MS peaks differentiating sample categories. BMC Res Notes. 2013;6:109

25. Patino WD, Mian OY, Kang JG, Matoba S, Bartlett LD, Holbrook B, Trout HH 3rd, Kozloff L, Hwang PM. Circulating transcriptome reveals markers of atherosclerosis. Proc Natl Acad Sci U S A. 2005;102(9):3423-8

26. West M, Blanchette C, Dressman H, Huang E, Ishida S, Spang R, Zuzan H, Olson JA Jr, Marks JR, Nevins JR. Predicting the clinical status of human breast cancer by using gene expression profiles. Proc Natl Acad Sci U S A. 2001;98(20):11462-7

27. Nguyen DV, Rocke DM. Partial least squares proportional hazard regression for application to DNA microarray survival data.Bioinformatics. 2002;18(12):1625-32

28. Li L, Darden TA, Weinberg CR, Levine AJ, Pedersen LG. Gene assessment and sample classification for gene expression data using a genetic algorithm/k-nearest neighbor method. Comb Chem High Throughput Screen. 2001;4(8):727-39

29. Deschamps AM, Spinale FG. Pathways of matrix metalloproteinase induction in heart failure: bioactive molecules and transcriptional regulation. Cardiovasc Res. 2006;69(3):666-76

30. Jia P, Zheng S, Long J, Zheng W, Zhao Z. dmGWAS: dense module searching for genome-wide association studies in protein-protein interaction networks. Bioinformatics. 2011;27(1):95-102

31. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498-504

32. Al-Shahrour F, Minguez P, Tarraga J, Montaner D, Alloza E, Vaquerizas JM, Conde L, Blaschke C, Vera J, Dopazo J.BABELOMICS: a systems biology perspective in the functional annotation of genome-scale experiments. Nucleic Acids Res.2006;34(Web Server issue):W472-6

33. Miwa M, Ohta T, Rak R, Rowley A, Kell DB, Pyysalo S, Ananiadou S. A method for integrating and ranking the evidence for biochemical pathways by mining reactions from text. Bioinformatics. 2013;29(13):i44-i52

34. Waters KM, Liu T, Quesenberry RD, Willse AR, Bandyopadhyay S, Kathmann LE, Weber TJ, Smith RD, Wiley HS, Thrall BD.Network analysis of epidermal growth factor signaling using integrated genomic, proteomic and phosphorylation data. PLoS One. 2012;7(3):e34515

35. Jain N, Thatte J, Braciale T, Ley K, O’Connell M, Lee JK. Local-pooled-error test for identifying differentially expressed genes with a small number of replicated microarrays. Bioinformatics. 2003;19(15):1945-51

36. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A. 2001;98(9):5116-21

37. Nacheva EP, Grace CD, Brazma D, Gancheva K, Howard-Reeves J, Rai L, Gale RE, Linch DC, Hills RK, Russell N, Burnett AK, Kottaridis PD. Does BCR/ABL1 positive acute myeloid leukaemia exist?. Br J Haematol. 2013;161(4):541-50

38. Wolfinger RD, Gibson G, Wolfinger ED, Bennett L, Hamadeh H, Bushel P, Afshari C, Paules RS. Assessing gene significance from cDNA microarray expression data via mixed models. J Comput Biol. 2001;8(6):625-37

39. Darvin K, Randolph A, Ovalles S, Halade D, Breeding L, Richardson A, Espinoza SE. Plasma Protein Biomarkers of the Geriatric Syndrome of Frailty. J Gerontol A Biol Sci Med Sci.

40. Kerr MK, Churchill GA. Bootstrapping cluster analysis: assessing the reliability of conclusions from microarray experiments.Proc Natl Acad Sci U S A. 2001;98(16):8961-5

41. Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci U S A. 2003;100(16):9440-5

42. Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44-57

43. Soukup M, Lee JK. Developing optimal prediction models for cancer classification using gene expression data. J Bioinform Comput Biol. 2004;1(4):681-94

44. Huang Y, Pepe MS, Feng Z. Logistic Regression Analysis with Standardized Markers. Ann Appl Stat. 2013 7 (3)

45. Fattovich G, Stroffolini T, Zagni I, Donato F. Hepatocellular carcinoma in cirrhosis: incidence and risk factors.Gastroenterology. 2004;127(5 Suppl 1):S35-50

46. Junrong T, Huancheng Z, Feng H, Yi G, Xiaoqin Y, Zhengmao L, Hong Z, Jianying Z, Yin W, Yuanhang H, Jianlin Z, Longhua S, Guolin H. Proteomic identification of CIB1 as a potential diagnostic factor in hepatocellular carcinoma. J Biosci. 2011;36(4):659-68

47. Mehan MR, Ostroff R, Wilcox SK, Steele F, Schneider D, Jarvis TC, Baird GS, Gold L, Janjic N. Highly multiplexed proteomic platform for biomarker discovery, diagnostics, and therapeutics. Adv Exp Med Biol. 2013;734:283-300

48. Di Deco J, Gonzalez AM, Diaz J, Mato V, Garcia-Frank D, Alvarez-Linera J, Frank A, Hernandez-Tamames JA. Machine learning and social network analysis applied to Alzheimer’s disease biomarkers. Curr Top Med Chem. 2013;13(5):652-62

49. Minati L, Varotto G, D’Incerti L, Panzica F, Chan D. From brain topography to brain topology: relevance of graph theory to functional neuroscience. Neuroreport. 2013;24(10):536-43

50. Bleris L, Xie Z, Glass D, Adadey A, Sontag E, Benenson Y. Synthetic incoherent feedforward circuits show adaptation to the amount of their genetic template. Mol Syst Biol;7:519.

51. Ma’ayan A, Blitzer RD, Iyengar R. Toward predictive models of mammalian cells. Annu Rev Biophys Biomol Struct. 2005;34:319-49

52. Sivachenko AY, Yuryev A, Daraselia N, Mazo I. Molecular networks in microarray analysis. J Bioinform Comput Biol.2007;5(2B):429-56

53. Wang H, Zheng H, Azuaje F. Ontology- and graph-based similarity assessment in biological networks. Bioinformatics.2010;26(20):2643-4

54. Saito R, Smoot ME, Ono K, Ruscheinski J, Wang PL, Lotia S, Pico AR, Bader GD, Ideker T. A travel guide to Cytoscape plugins.Nat Methods. 2012;9(11):1069-76

55. Wang Q, Tang B, Song L, Ren B, Liang Q, Xie F, Zhuo Y, Liu X, Zhang L. 3DScapeCS: application of three dimensional, parallel, dynamic network visualization in Cytoscape. BMC Bioinformatics. 2013;14(1):322

56. Ganter B, Zidek N, Hewitt PR, Muller D, Vladimirova A. Pathway analysis tools and toxicogenomics reference databases for risk assessment. Pharmacogenomics. 2008;9(1):35-54

57. Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C, Jensen LJ.STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res.2013;41(Database issue):D808-15

58. Pe’er D. Bayesian network analysis of signaling networks: a primer. Sci STKE 2005. 2005(281):pl4

59. Larjo A, Shmulevich I, Lahdesmaki H. Structure learning for Bayesian networks as models of biological networks. Methods Mol Biol. 2013;939:35-45

60. Han B, Chen XW, Talebizadeh Z, Xu H. Genetic studies of complex human diseases: characterizing SNP-disease associations using Bayesian networks. BMC Syst Biol. 2013;6(Suppl 3):S14

61. Mehri M. A comparison of neural network models, fuzzy logic, and multiple linear regression for prediction of hatchability.Poult Sci. 2013;92(4):1138-42

62. Jinlian Wang HWR. Bayesian Network for Omics Data Integration. Washington DC: GENISP. 2012:110-3

63. Alterovitz G, Liu J, Afkhami E, Ramoni MF. Bayesian methods for proteomics. Proteomics. 2007;7(16):2843-55

64. Barnes CP, Silk D, Sheng X, Stumpf MP. Bayesian design of synthetic biological systems. Proc Natl Acad Sci U S A.2011;108(37):15190-5

65. Terfve C, Saez-Rodriguez J. Modeling signaling networks using high-throughput phospho-proteomics. Adv Exp Med Biol.2012;736:19-57

66. Hill SM, Lu Y, Molina J, Heiser LM, Spellman PT, Speed TP, Gray JW, Mills GB, Mukherjee S. Bayesian inference of signaling network topology in a cancer cell line. Bioinformatics. 2012;28(21):2804-10

67. Husmeier D. Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics. 2003;19(17):2271-82

68. Rau A, Jaffrezic F, Foulley JL, Doerge RW. An empirical Bayesian method for estimating biological networks from temporal microarray data. Stat Appl Genet Mol Biol. 2010;9:Article 9

69. Mukherjee S, Speed TP. Network inference using informative priors. Proc Natl Acad Sci U S A. 2008;105(38):14313-8

70. Ciaccio MF, Wagner JP, Chuu CP, Lauffenburger DA, Jones RB. Systems analysis of EGF receptor signaling dynamics with microwestern arrays. Nat Methods. 2010;7(2):148-55

71. Friedman N, Linial M, Nachman I, Pe’er D. Using Bayesian networks to analyze expression data. J Comput Biol. 2000;7(3-4):601-20

72. Santra T, Kolch W, Kholodenko BN. Integrating Bayesian variable selection with Modular Response Analysis to infer biochemical network topology. BMC Syst Biol. 2013;7:57

73. Santra T, Kolch W, Kholodenko BN. Integrating Bayesian variable selection with Modular Response Analysis to infer biochemical network topology. BMC Syst Biol. 2010;7:57

74. Hill SM, Neve RM, Bayani N, Kuo WL, Ziyad S, Spellman PT, Gray JW, Mukherjee S. Integrating biological knowledge into variable selection: an empirical Bayes approach with an application in cancer biology. BMC Bioinformatics. 2010;13:94

75. Perez-Iratxeta C, Wjst M, Bork P, Andrade MA. G2D: a tool for mining genes associated with disease. BMC Genet. 2005;6:45

76. Perez-Iratxeta C, Bork P, Andrade MA. Association of genes to genetically inherited diseases using data mining. Nat Genet.2002;31(3):316-9

77. Blagosklonny MV, Pardee AB. Conceptual biology: unearthing the gems. Nature. 2002;416(6879):373

78. van Haagen HH, t Hoen PA, Botelho Bovo A, de Morree A, van Mulligen EM, Chichester C, Kors JA, den Dunnen JT, van Ommen GJ, van der Maarel SM, Kern VM, Mons B, Schuemie MJ. Novel protein-protein interactions inferred from literature context. PLoS One. 2009;4(11):e7894

79. Elkin PL, Tuttle MS, Trusko BE, Brown SH. BioProspecting: novel marker discovery obtained by mining the bibleome. BMC Bioinformatics. 2009;10(Suppl 2):S9

80. Frijters R, Heupers B, van Beek P, Bouwhuis M, van Schaik R, de Vlieg J, Polman J, Alkema W. CoPub: a literature-based keyword enrichment tool for microarray data analysis. Nucleic Acids Res. 2008;36(Web Server issue):W406-10

81. Schwartz JM, Gaugain C, Nacher JC, de Daruvar A, Kanehisa M. Observing metabolic functions at the genome scale. Genome Biol. 2007;8(6):R123

82. Nikitin A, Egorov S, Daraselia N, Mazo I. Pathway studio–the analysis and navigation of molecular networks. Bioinformatics.2003;19(16):2155-7

83. Keshava Prasad TS, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A, Balakrishnan L, Marimuthu A, Banerjee S, Somanathan DS, Sebastian A, Rani S, Ray S, Harrys Kishore CJ, Kanth S, Ahmed M, Kashyap MK, Mohmood R, Ramachandra YL, Krishna V, Rahiman BA, Mohan S, Ranganathan P, Ramabadran S, Chaerkady R, Pandey A. Human Protein Reference Database–2009 update. Nucleic Acids Res. 2009;37(Database issue):D767-72

84. Wang J YH, Tadesse MG, Ressom HW. A Bayesian network model for omics data integration. Proceedings of the 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). Washington DC. 2012

85. Cohen KB, Johnson HL, Verspoor K, Roeder C, Hunter LE. The structural and content aspects of abstracts versus bodies of full text journal articles are different. BMC Bioinformatics. 2010;11:492

86. Lev I, Volpe M, Goor L, Levinton N, Emuna L, Ben-Aroya S. Reverse PCA, a Systematic Approach for Identifying Genes Important for the Physical Interaction between Protein Pairs. PLoS Genet. 2013;9(10):e1003838

87. Li T, Zhu S, Shuai L, Xu Y, Yin S, Bian Y, Wang Y, Zuo B, Wang W, Zhao S, Zhang L, Zhang J, Gao GF, Allain JP, Li C. Infection of common marmosets with hepatitis C virus/GB virus-B chimeras. Hepatology. 2013

88. White NM, Newsted DW, Masui O, Romaschin AD, Siu KW, Yousef GM. Identification and validation of dysregulated metabolic pathways in metastatic renal cell carcinoma. Tumour Biol. 2013

89. Tang H, Wei P, Duell EJ, Risch HA, Olson SH, Bueno-de-Mesquita HB, Gallinger S, Holly EA, Petersen GM, Bracci PM, McWilliams RR, Jenab M, Riboli E, Tjonneland A, Boutron-Ruault MC, Kaaks R, Trichopoulos D, Panico S, Sund M, Peeters PH, Khaw KT, Amos CI, Li D. Genes-environment interactions in obesity- and diabetes-associated pancreatic cancer: A GWAS data analysis. Cancer Epidemiol Biomarkers Prev. 2013

90. Koo CL, Liew MJ, Mohamad MS, Mohamed Salleh AH. A Review for Detecting Gene-Gene Interactions Using Machine Learning Methods in Genetic Epidemiology. Biomed Res Int. 2013;2013:432375

91. Yang ZR. Neural networks. Methods Mol Biol. 2010;609:197-222

92. Zhang F, Kaufman HL, Deng Y, Drabier R. Recursive SVM biomarker selection for early detection of breast cancer in peripheral blood. BMC Med Genomics. 2013;6(Suppl 1):S4

93. Mattison HA, Stewart T, Zhang J. Applying bioinformatics to proteomics: is machine learning the answer to biomarker discovery for PD and MSA?. Mov Disord. 2012;27(13):1595-7

94. Andreyev AY, Shen Z, Guan Z, Ryan A, Fahy E, Subramaniam S, Raetz CR, Briggs S, Dennis EA. Application of proteomic marker ensembles to subcellular organelle identification. Mol Cell Proteomics. 2010;9(2):388-402

95. Chattopadhyay S, Bagchi P, Dutta D, Mukherjee A, Kobayashi N, Chawlasarkar M. Computational identification of post-translational modification sites and functional families reveal possible moonlighting role of rotaviral proteins. Bioinformation.2010;4(10):448-51

96. Crooks GE, Wolfe J, Brenner SE. Measurements of protein sequence-structure correlations. Proteins. 2004;57(4):804-10

97. Wang J, Yu Y, Zhao Y, Zhang D, Li J. Evaluation and integration of existing methods for computational prediction of allergens. BMC Bioinformatics. 2013;14(Suppl 4):S1

98. Werfel J, Krause S, Bischof AG, Mannix RJ, Tobin H, Bar-Yam Y, Bellin RM, Ingber DE. How changes in extracellular matrix mechanics and gene expression variability might combine to drive cancer progression. PLoS One. 2013;8(10):e76122

99. Wong SH, Sung JJ, Chan FK, To KF, Ng SS, Wang XJ, Yu J, Wu WK. Genome-wide association and sequencing studies on colorectal cancer. Semin Cancer Biol. 2013

100. Han H. A novel profile biomarker diagnosis for mass spectral proteomics. Pac Symp Biocomput. 2014;19:340-51

101. Hoshida Y, Nijman SM, Kobayashi M, Chan JA, Brunet JP, Chiang DY, Villanueva A, Newell P, Ikeda K, Hashimoto M, Watanabe G, Gabriel S, Friedman SL, Kumada H, Llovet JM, Golub TR. Integrative transcriptome analysis reveals common molecular subclasses of human hepatocellular carcinoma. Cancer Res. 2009;69(18):7385-92

102. Ideker T, Krogan NJ. Differential network biology. Mol Syst Biol. 2012;8:565

103. Park SJ, Lee SY, Cho J, Kim TY, Lee JW, Park JH, Han MJ. Global physiological understanding and metabolic engineering of microorganisms based on omics studies. Appl Microbiol Biotechnol. 2005;68(5):567-79

104. Oishi N, Kumar MR, Roessler S, Ji J, Forgues M, Budhu A, Zhao X, Andersen JB, Ye QH, Jia HL, Qin LX, Yamashita T, Woo HG, Kim YJ, Kaneko S, Tang ZY, Thorgeirsson SS, Wang XW. Transcriptomic profiling reveals hepatic stem-like gene signatures and interplay of miR-200c and epithelial-mesenchymal transition in intrahepatic cholangiocarcinoma. Hepatology. 2012;56(5):1792-803

105. Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, Kasif S, Collins JJ, Gardner TS. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 2007;5(1):e8

106. McDermott JE, Diamond DL, Corley C, Rasmussen AL, Katze MG, Waters KM. Topological analysis of protein co-abundance networks identifies novel host targets important for HCV infection and pathogenesis. BMC Syst Biol. 2012;6:28

107. McDermott JE, Taylor RC, Yoon H, Heffron F. Bottlenecks and hubs in inferred networks are important for virulence in Salmonella typhimurium. J Comput Biol. 2009;16(2):169-80

108. Chen MH, Yang WL, Lin KT, Liu CH, Liu YW, Huang KW, Chang PM, Lai JM, Hsu CN, Chao KM, Kao CY, Huang CY. Gene expression-based chemical genomics identifies potential therapeutic drugs in hepatocellular carcinoma. PLoS One.2011;6(11):e27186

109. Coban Z, Barton MC. Integrative genomics: liver regeneration and hepatocellular carcinoma. J Cell Biochem.2013;113(7):2179-84

110. Mitchell HD, Eisfeld AJ, Sims AC, McDermott JE, Matzke MM, Webb-Robertson BJ, Tilton SC, Tchitchek N, Josset L, Li C, Ellis AL, Chang JH, Heegel RA, Luna ML, Schepmoes AA, Shukla AK, Metz TO, Neumann G, Benecke AG, Smith RD, Baric RS, Kawaoka Y, Katze MG, Waters KM. A network integration approach to predict conserved regulators related to pathogenicity of influenza and SARS-CoV respiratory viruses. PLoS One. 2013;8(7):e69374

111. Wang J, Chen L, Tian X, Gao L, Niu X, Shi M, Zhang W. Global Metabolomic and Network analysis of Escherichia coli Responses to Exogenous Biofuels. J Proteome Res. 2013

112. Notebaart RA, Teusink B, Siezen RJ, Papp B. Co-regulation of metabolic genes is better explained by flux coupling than by network distance. PLoS Comput Biol. 2008;4(1):e26

113. Shlomi T, Cabili MN, Herrgard MJ, Palsson BO, Ruppin E. Network-based prediction of human tissue-specific metabolism. Nat Biotechnol. 2008;26(9):1003-10

114. Blum T, Kohlbacher O. MetaRoute: fast search for relevant metabolic routes for interactive network navigation and visualization. Bioinformatics. 2008;24(18):2108-9

115. Blazier AS, Papin JA. Integration of expression data in genome-scale metabolic network reconstructions. Front Physiol.2012;3:299

116. Federici G, Gao X, Slawek J, Arodz T, Shitaye A, Wulfkuhle JD, De Maria R, Liotta LA, Petricoin EF 3rd. Systems analysis of the NCI-60 cancer cell lines by alignment of protein pathway activation modules with “-OMIC” data fields and therapeutic response signatures. Mol Cancer Res. 2013;11(6):676-85

117. Cui J, Liu J, Li Y, Shi T. Integrative identification of Arabidopsis mitochondrial proteome and its function exploitation through protein interaction network. PLoS One. 2011;6(1):e16022

118. Hallock P, Thomas MA. Integrating the Alzheimer’s disease proteome and transcriptome: a comprehensive network model of a complex disease. Omics. 2012;16(1-2):37-49

119. Waters KM, Pounds JG, Thrall BD. Data merging for integrated microarray and proteomic analysis. Brief Funct Genomic Proteomic. 2006;5(4):261-72

120. Zhou B, Wang J, Ressom HW. MetaboSearch: tool for mass-based metabolite identification using multiple databases. PLoS One. 2012;7(6):e40096

121. Llovet JM, Di Bisceglie AM, Bruix J, Kramer BS, Lencioni R, Zhu AX, Sherman M, Schwartz M, Lotze M, Talwalkar J, Gores GJ.Design and endpoints of clinical trials in hepatocellular carcinoma. J Natl Cancer Inst. 2008;100(10):698-711

122. Llovet JM, Bruix J. Novel advancements in the management of hepatocellular carcinoma in 2008. J Hepatol. 2008;48(Suppl 1):S20-37

123. Llovet JM, Ricci S, Mazzaferro V, Hilgard P, Gane E, Blanc JF, de Oliveira AC, Santoro A, Raoul JL, Forner A, Schwartz M, Porta C, Zeuzem S, Bolondi L, Greten TF, Galle PR, Seitz JF, Borbath I, Haussinger D, Giannaris T, Shan M, Moscovici M, Voliotis D, Bruix J.Sorafenib in advanced hepatocellular carcinoma. N Engl J Med. 2008;359(4):378-90

124. Zhang DY, Ye F, Gao L, Liu X, Zhao X, Che Y, Wang H, Wang L, Wu J, Song D, Liu W, Xu H, Jiang B, Zhang W, Wang J, Lee P.Proteomics, pathway array and signaling network-based medicine in cancer. Cell Div. 2009;4:20

125. Chang JT, Carvalho C, Mori S, Bild AH, Gatza ML, Wang Q, Lucas JE, Potti A, Febbo PG, West M, Nevins JR. A genomic strategy to elucidate modules of oncogenic pathway signaling networks. Mol Cell. 2009;34(1):104-14

126. He X, Wei Q, Sun M, Fu X, Fan S, Li Y. LS-CAP: an algorithm for identifying cytogenetic aberrations in hepatocellular carcinoma using microarray data. Front Biosci. 2006;11:1311-22

127. Poon TC, Wong N, Lai PB, Rattray M, Johnson PJ, Sung JJ. A tumor progression model for hepatocellular carcinoma: bioinformatic analysis of genomic data. Gastroenterology. 2006;131(4):1262-70

128. Ramos H, Shannon P, Brusniak MY, Kusebauch U, Moritz RL, Aebersold R. The Protein Information and Property Explorer 2: gaggle-like exploration of biological proteomic data within one webpage. Proteomics. 2012;11(1):154-8

129. Yin S, Li J, Hu C, Chen X, Yao M, Yan M, Jiang G, Ge C, Xie H, Wan D, Yang S, Zheng S, Gu J. CD133 positive hepatocellular carcinoma cells possess high capacity for tumorigenicity. Int J Cancer. 2007;120(7):1444-50

130. Zhang Y, Wang S, Li D, Zhnag J, Gu D, Zhu Y, He F. A systems biology-based classifier for hepatocellular carcinoma diagnosis. PLoS One. 2011;6(7):e22426

131. Hollywood K, Brison DR, Goodacre R. Metabolomics: current technologies and future trends. Proteomics. 2006;6(17):4716-23

132. Hsu CN, Lai JM, Liu CH, Tseng HH, Lin CY, Lin KT, Yeh HH, Sung TY, Hsu WL, Su LJ, Lee SA, Chen CH, Lee GC, Lee DT, Shiue YL, Yeh CW, Chang CH, Kao CY, Huang CY. Detection of the inferred interaction network in hepatocellular carcinoma from EHCO (Encyclopedia of Hepatocellular Carcinoma genes Online). BMC Bioinformatics. 2007;8:66

133. Su WH, Chao CC, Yeh SH, Chen DS, Chen PJ, Jou YS. OncoDB.HCC: an integrated oncogenomic database of hepatocellular carcinoma revealed aberrant cancer target genes and loci. Nucleic Acids Res. 2007;35(Database issue):D727-31

134. Kwofie SK, Schaefer U, Sundararajan VS, Bajic VB, Christoffels A. HCVpro: hepatitis C virus protein interaction database.Infect Genet Evol. 2011;11(8):1971-7

135. Combet C, Bettler E, Terreux R, Garnier N, Deleage G. The euHCVdb suite of in silico tools for investigating the structural impact of mutations in hepatitis C virus proteins. Infect Disord Drug Targets. 2009;9(3):272-8

136. Shin IT, Tanaka Y, Tateno Y, Mizokami M. Development and public release of a comprehensive hepatitis virus database.Hepatol Res. 2008;38(3):234-43

137. Kuiken C, Yusim K, Boykin L, Richardson R. The Los Alamos hepatitis C sequence database. Bioinformatics. 2005;21(3):379-84

138. Zhang Y, Yang C, Wang S, Chen T, Li M, Wang X, Li D, Wang K, Ma J, Wu S, Zhang X, Zhu Y, Wu J, He F. LiverAtlas: a unique integrated knowledge database for systems-level research of liver and hepatic disease. Liver Int. 2013

139. Yu XJ, Fang F, Tang CL, Yao L, Yu L, Yu L. dbHCCvar: a comprehensive database of human genetic variations in hepatocellular carcinoma. Hum Mutat. 2011;32(12):E2308-16

140. Ransohoff DF. Proteomics research to discover markers: what can we learn from Netflix?. Clin Chem. 2011;56(2):172-6

141. Singhal A, Jayaraman M, Dhanasekaran DN, Kohli V. Molecular and serum markers in hepatocellular carcinoma: predictive tools for prognosis and recurrence. Crit Rev Oncol Hematol. 2011;82(2):116-40

142. Sreekumar A, Poisson LM, Rajendiran TM, Khan AP, Cao Q, Yu J, Laxman B, Mehra R, Lonigro RJ, Li Y, Nyati MK, Ahsan A, Kalyana-Sundaram S, Han B, Cao X, Byun J, Omenn GS, Ghosh D, Pennathur S, Alexander DC, Berger A, Shuster JR, Wei JT, Varambally S, Beecher C, Chinnaiyan AM. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature. 2009;457(7231):910-4

143. Jentzmik F, Stephan C, Lein M, Miller K, Kamlage B, Bethan B, Kristiansen G, Jung K. Sarcosine in prostate cancer tissue is not a differential metabolite for prostate cancer aggressiveness and biochemical progression. J Urol. 2011;185(2):706-11

144. Sreekumar A, Poisson LM, Rajendiran TM, Khan AP, Cao Q, Yu J, Laxman B, Mehra R, Lonigro RJ, Li Y, Nyati MK, Ahsan A, Kalyana-Sundaram S, Han B, Cao X, Byun J, Omenn GS, Ghoshd D, Pennathur S, Alexander DC, Berger A, Shuster JR, Wei JT, Varambally S, Beecher C, Chinnaiyan AM. Re: Florian Jentzmik, Carsten Stephan, Kurt Miller, et al. Sarcosine in urine after digital rectal examination fails as a marker in prostate cancer detection and identification of aggressive tumours. Eur Urol. 2010;58:12-8

145. Beger RD, Sun J, Schnackenberg LK. Metabolomics approaches for discovering biomarkers of drug-induced hepatotoxicity and nephrotoxicity. Toxicol Appl Pharmacol. 2010;243(2):154-66

146. Sanefuji K, Taketomi A, Iguchi T, Sugimachi K, Ikegami T, Yamashita Y, Gion T, Soejima Y, Shirabe K, Maehara Y. Significance of DNA polymerase delta catalytic subunit p125 induced by mutant p53 in the invasive potential of human hepatocellular carcinoma. Oncology. 2010;79(3-4):229-37

Author contact

Corresponding address Corresponding author: Habtom W Ressom, PhD. Professor, Department of Oncology Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Room 175 Building D, 4000 Reservoir Road, NW, Washington, DC 20057. Email: hwr@georgetown.edu Telephone: (202) 687-2283 Telefax: (202) 687-0227.


Received 2014-9-24
Accepted 2014-11-14
Published 2015-1-1


UCSD 的生物信息學算法

生物信息學算法(第 1 部分)

This course was the first in a two-part series covering some of the algorithms underlying bioinformatics. It has now been split into three smaller courses.


課程概述

The sequencing of the human genome fueled a computational revolution in biology. As a result, modern biology produces as many new algorithms as any other fundamental realm of science.  Accordingly, the newly formed links between computer science and biology affect the way we teach applied algorithms to computer scientists.

This course has now been split into three smaller pieces:

  • Finding Hidden Messages in DNA: This course begins a series of classes illustrating the power of computing in modern biology. Please join us on the frontier of bioinformatics to look for hidden messages in DNA without ever needing to put on a lab coat. After warming up our algorithmic muscles, we will learn how randomized algorithms can be used to solve problems in bioinformatics.
  • Assembling Genomes and Sequencing Antibiotics: Biologists still cannot read the nucleotides of an entire genome or the amino acids of an antibiotic as you would read a book from beginning to end. However, they can read short pieces of DNA and weigh small antibiotic fragments. In this course, we will see how graph theory and brute force algorithms can be used to reconstruct genomes and antibiotics.
  • Comparing Genes, Proteins, and Genomes: After sequencing genomes, we would like to compare them. We will see that dynamic programming is a powerful algorithmic tool when we compare two genes or two proteins. When we “zoom out” to compare entire genomes, we will employ combinatorial algorithms.

Each course parallels two chapters from a textbook covering a single biological question and slowly builds the algorithmic knowledge required to address this challenge.  Along the way, coding challenges and exercises (many of which ask you to apply your skills to real genetic data) will be directly integrated into the text at the exact moment they are needed.

授課大綱

The course was based on six “chapters” covering the following central questions, with the algorithmic ideas that we will use to solve them in parentheses:

  • Where Does DNA Replication Begin? (Algorithmic Warm-up)
  • How Do We Sequence Antibiotics? (Brute Force Algorithms)
  • Which DNA Patterns Act As Cellular Clocks? (Greedy and Randomized Algorithms)
  • How Do We Assemble Genomes? (Graph Algorithms)
  • How Do We Compare Biological Sequences? (Dynamic Programming Algorithms)
  • Are There Fragile Regions in the Human Genome? (Combinatorial Algorithms)

Bioinformatics Algorithms (Part 2) is based around the following questions:

  • Which Animal Gave Us SARS? (Evolutionary Trees)
  • How Do We Locate Disease-Causing Mutations? (Combinatorial Pattern Matching)
  • How Did Yeast Become Such a Good Wine Brewer? (Clustering Algorithms)
  • Why Do We Still Not Have an HIV Vaccine? (Hldden Markov Models)
  • Was T-Rex Just a Big Chicken? (Computational Proteomics)
  • What Genetic Characteristics Do Human Populations Share? (Principal Components Analysis)

先修知識

You should know the basics of programming in the language of your choice. We have the following suggestions for resources that will help you learn programming.

There are many other online resources for learning programming, and we encourage you to seek them out yourself!

參考資料

 Bioinformatics Algorithms: An Active-Learning Approach, by Compeau & Pevzner.

授課形式

The class offered two ways of learning the material.  In addition to a collection of lecture videos, the primary content for the course was the textbook Bioinformatics Algorithms: An Active-Learning Approach, by Phillip Compeau & Pavel Pevzner.

常見問題解答

Q: Why was this course split into three courses?

Based on survey feedback, completion data, and studies of other courses, we realized that having shorter courses gives our students more flexibility around their busy schedules. Even though the courses have been split, the overall content remains the same, so we feel confident that we’re maintaining learning standards of our material.

Q: What if I earned a voucher for retaking this course? Can I use it in the new courses?

Vouchers from the older course will be valid for the newer courses. If you took the original course and earned a voucher, you will be issued a voucher for this course as well as for “Assembling Genomes and Sequencing Antibiotics” and “Comparing Genes, Proteins, and Genomes”  (three vouchers total).

Q: Does this mean that the overall cost for earning Verified Certificates is greater now?

Yes. Since there are more courses now, the overall cost for Verified Certificates is greater than before. Coursera offers a Financial Aid program for learners who would face a serious hardship paying for our courses. Plus, if you just want to join and check out our course content, it’s still free and available to everyone.

 

詳細資訊請連結以下的網址:

https://www.coursera.org/course/bioinformatics