Pressing needs of biomedical text mining in biocuration and beyond: Opportunities and challenges

Database

Volumn 2016, Issue , 2016, Pages

  Singhal, Ayush ^a   Leaman, Robert ^a   Catlett, Natalie ^b   Lemberger, Thomas ^c   McEntyre, Johanna ^d   Polson, Shawn ^e   Xenarios, Ioannis ^f   Arighi, Cecilia ^a,e   Lu, Zhiyong ^a  

^a NATIONAL INSTITUTES OF HEALTH   (United States)

^b Selventa   (United States)

^c EMBO   (Germany)

^d EUROPEAN BIOINFORMATICS INSTITUTE   (United Kingdom)

^e University of Delaware   (United States)

^f SWISS INSTITUTE OF BIOINFORMATICS   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; INFORMATION PROCESSING; MEDICAL RESEARCH; PROCEDURES;

BIOMEDICAL RESEARCH; DATA CURATION; DATA MINING;

EID: 85016108264     PISSN: 17580463     EISSN: None     Source Type: Journal    
DOI: 10.1093/database/baw161     Document Type: Review

References (59)

1. UniProt, C. (2015) UniProt: a hub for protein information. Nucleic Acids Res., 43, D204-D212.
2. NCBI Resource Coordinators (2015) Database resources of the National Center for Biotechnology Information. Nucleic Acids Res., 43, D6-D17.
3. Baxevanis, A.D., and Bateman, A. (2006) The importance of biological databases in biological discovery. Curr. Protoc. Bioinformatics, 50, 1.1.1.-1.1.8.
4. Wei, C.H., Kao, H.Y., and Lu, Z. (2013) PubTator: a web-based text mining tool for assisting biocuration. Nucleic Acids Res., 41, W518-W522.
5. The Europe PMC Consortium (2015) Europe PMC: a full-text literature database for the life sciences and platform for innovation. Nucleic Acids Res., 43, D1042-D1048.
6. Lemberger, T. (2014) Tools of discovery. Mol. Syst. Biol., 10, 715.
7. Hirschman, L., Burns, G.A., Krallinger, M. et al. (2012) Text mining for the biocuration workflow. Database (Oxford), 2012, bas020.
8. Lu, Z., and Hirschman, L. (2012) Biocuration workflows and text mining: overview of the BioCreative 2012 Workshop Track II. Database (Oxford), 2012, bas043.
9. Van Auken, K., Jaffery, J., Chan, J. et al. (2009) Semi-automated curation of protein subcellular localization: a text mining-based approach to Gene Ontology (GO) Cellular Component curation. BMC Bioinformatics, 10, 228.
10. Van Auken, K., Fey, P., Berardini, T.Z. et al. (2012) Text mining in the biocuration workflow: applications for literature curation at WormBase, dictyBase and TAIR. Database (Oxford), 2012, bas040.
11. Wei, C.H., Harris, B.R., Li, D. et al. (2012) Accelerating literature curation with text-mining tools: a case study of using PubTator to curate genes in PubMed abstracts. Database (Oxford), 2012, bas041.
12. Tudor, C.O., Arighi, C.N., Wang, Q. et al. (2012) The eFIP system for text mining of protein interaction networks of phosphorylated proteins. Database (Oxford), 2012, bas044.
13. Cejuela, J.M., McQuilton, P., Ponting, L. et al. (2014) tagtog: interactive and text-mining-assisted annotation of gene mentions in PLOS full-text articles. Database (Oxford), 2014, bau033.
14. Arighi, C.N., Roberts, P.M., Agarwal, S. et al. (2011) BioCreative III interactive task: an overview. BMC Bioinformatics, 12, S4.
15. Arighi, C.N., Carterette, B., Cohen, K.B. et al. (2013) An overview of the BioCreative 2012 Workshop Track III: interactive text mining task. Database (Oxford), 2013, bas056.
16. Matis-Mitchell, S., Roberts, P., Tudor, C.O., and Arighi, C.N. (2013) BioCreative IV interactive task. Proceedings of the Fourth BioCreative Challenge Evaluation Workshop, vol 1. Bethesda, MD, pp. 190-203.
17. Wang, Q.H., Abdul, S.S., Almeida, L. et al. (2016) Overview of the interactive task in BioCreative V. Database (Oxford), 2016, baw119.
18. Liechti, R., George, N., El-Gebali, S. et al. (2016) SourceData - a semantic platform for curating and searching figures. bioRxiv, 058529.
19. Singhal, A., Kasturi, R., Sivakumar, V. et al. (2013) Leveraging web intelligence for finding interesting research datasets. Proceedings of the International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). 2013 IEEE/WIC/ACM. IEEE, vol. 1, pp. 321-328.
20. Singhal, A. (2014) Leveraging Open Source Web Resources to Improve Retrieval of Low Text Content Items. Department of Computer science. University of Minnesota, Minneapolis, MN, 144.
21. Martin, F., Thomson, T.M., Sewer, A. et al. (2012) Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks. BMC Syst. Biol., 6, 54.
22. Thomson, T.M., Sewer, A., Martin, F. et al. (2013) Quantitative assessment of biological impact using transcriptomic data and mechanistic network models. Toxicol. Appl. Pharmacol., 272, 863-878.
23. Catlett, N.L., Bargnesi, A.J., Ungerer, S. et al. (2013) Reverse causal reasoning: applying qualitative causal knowledge to the interpretation of high-throughput data. BMC Bioinformatics, 14, 340.
24. Boue, S., Talikka, M., Westra, J.W. et al. (2015) Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems. Database (Oxford), 2015, bav030.
25. Fluck, J., Madan, S., Ansari, S. et al. (2014) BELIEF-a Semiautomatic Workflow for BEL Network Creation. Proceedings of the 6th International Symposium on Semantic Mining in Biomedicine (SMBM), University of Aveiro, Portugal, 109-113.
26. Gomez, A., Rothman, J.M., Petrzelkova, K. et al. (2016) Temporal variation selects for diet-microbe co-metabolic traits in the gut of Gorilla spp. ISME J., 10, 532.
27. Sampson, T.R., and Mazmanian, S.K. (2015) Control of brain development, function, and behavior by the microbiome. Cell Host Microbe, 17, 565-576.
28. Neish, A.S. (2014) Mucosal immunity and the microbiome. Ann. Am. Thoracic Soc., 11, S28-S32.
29. McDermott, A.J., and Huffnagle, G.B. (2014) The microbiome and regulation of mucosal immunity. Immunology, 142, 24-31.
30. Human Microbiome Project Consortium (2012) Structure, function and diversity of the healthy human microbiome. Nature, 486, 207-214.
31. Schulz, M.D., Atay, C., Heringer, J. et al. (2014) High-fat-dietmediated dysbiosis promotes intestinal carcinogenesis independently of obesity. Nature, 514, 508-512.
32. Wommack, K.E., Bhavsar, J., Polson, S.W. et al. (2012) VIROME: a standard operating procedure for analysis of viral metagenome sequences. Stand. Genomic Sci., 6, 421-433.
33. Glass, E.M., Wilkening, J., Wilke, A. et al. (2010) Using the metagenomics RAST server (MG-RAST) for analyzing shotgun metagenomes. Cold Spring Harb. Protoc., 2010, pdb. prot5368.
34. Wu, C.H., Huang, H., Arminski, L. et al. (2002) The Protein Information Resource: an integrated public resource of functional annotation of proteins. Nucleic Acids Res., 30, 35-37.
35. Pafilis, E., Buttigieg, P.L., Ferrell, B. et al. (2016) EXTRACT: interactive extraction of environment metadata and term suggestion formetagenomic sample annotation. Database (Oxford), 2016.
36. Pafilis, E., Frankild, S.P., Schnetzer, J. et al. (2015) ENVIRONMENTS and EOL: identification of Environment Ontology terms in text and the annotation of the Encyclopedia of Life. Bioinformatics, 31, 1872-1874.
37. Buttigieg, P.L., Morrison, N., Smith, B. et al. (2013) The environment ontology: contextualising biological and biomedical entities. J. Biomed. Semant., 4, 43.
38. Leaman, R., and Lu, Z. (2016) TaggerOne: joint named entity recognition and normalization with semi-Markov models. Bioinformatics, 32, 2839-2846.
39. Wei, C.H., Kao, H.Y., and Lu, Z. (2012) SR4GN: a species recognition software tool for gene normalization. PloS ONE, 7, e38460.
40. Wiegers, T.C., Davis, A.P., and Mattingly, C.J. (2014) Web services-based text-mining demonstrates broad impacts for interoperability and process simplification. Database (Oxford), 2014, bau050.
41. Ferrucci, D., and Lally, A. (2004) UIMA: an architectural approach to unstructured information processing in the corporate research environment. Nat. Lang. Eng., 10, 327-348.
42. Verspoor, K., Baumgartner, W., Jr, Roeder, C. et al. (2009) Abstracting the types away from a UIMA type system. In C. Chiarcos, R. Eckhart de Castilho, M. Stede (Eds.), Form to Meaning: Processing Texts Automatically, pp. 249-256.
43. Comeau, D.C., Islamaj Dogan, R., Ciccarese, P. et al. (2013) BioC: a minimalist approach to interoperability for biomedical text processing. Database (Oxford), 2013, bat064.
44. Comeau, D.C., Batista-Navarro, R.T., Dai, H.J. et al. (2014) BioC interoperability track overview. Database (Oxford), 2014, bau053.
45. Pafilis, E., O'donoghue, S.I., Jensen, L.J. et al. (2009) Reflect: augmented browsing for the life scientist. Nat. Biotechnol., 27, 508-510.
46. Wei, C.H., Leaman, R., and Lu, Z. (2016) Beyond accuracy: creating interoperable and scalable text-mining web services. Bioinformatics, 32, 1907-1910.
47. Gerner, M., Nenadic, G., and Bergman, C.M. (2010) LINNAEUS: a species name identification system for biomedical literature. BMC Bioinformatics, 11, 85.
48. Gerner, M., Sarafraz, F., Bergman, C.M. et al. (2012) BioContext: an integrated text mining system for large-scale extraction and contextualization of biomolecular events. Bioinformatics, 28, 2154-2161.
49. Sun, Z., Errami, M., Long, T. et al. (2010) Systematic characterizations of text similarity in full text biomedical publications. PloS ONE, 5, e12704.
50. Thomas, P., Starlinger, J., Vowinkel, A. et al. (2012) GeneView: a comprehensive semantic search engine for PubMed. Nucleic Acids Res., 40, W585-W591.
51. Caporaso, J.G., Deshpande, N., Fink, J.L. et al. (2008) Intrinsic evaluation of text mining tools may not predict performance on realistic tasks. Pac Symp Biocomput, 640-651.
52. Cohen, K.B., Johnson, H.L., Verspoor, K. et al. (2010) The structural and content aspects of abstracts versus bodies of full text journal articles are different. BMC Bioinformatics, 11, 492.
53. Huang, C.C., and Lu, Z. (2016) Community challenges in biomedical text mining over 10 years: success, failure and the future. Brief. Bioinformatics, 17, 132-144.
54. Moult, J., Pedersen, J.T., Judson, R. et al. (1995) A large-scale experiment to assess protein structure prediction methods. Proteins, 23, ii-iv.
55. Moult, J., Fidelis, K., Kryshtafovych, A. et al. (2014) Critical assessment of methods of protein structure prediction (CASP)-round x. Proteins, 82, 1-6.
56. Mao, Y., Van Auken, K., Li, D. et al. (2014) Overview of the gene ontology task at BioCreative IV. Database (Oxford), 2014, bau086.
57. Huang, M., Neveol, A., and Lu, Z. (2011) Recommending MeSH terms for annotating biomedical articles. J. Am. Med. Inform. Assoc., 18, 660-667.
58. Lu, Z., Kao, H.Y., Wei, C.H. et al. (2011) The gene normalization task in BioCreative III. BMC Bioinformatics, 12, S2.
59. Morgan, A.A., Lu, Z., Wang, X. et al. (2008) Overview of BioCreative II gene normalization. Genome Biol., 9, S3.