Frontiers of biomedical text mining: Current progress

Briefings in Bioinformatics

Volumn 8, Issue 5, 2007, Pages 358-375

  Zweigenbaum, Pierre ^a   Demner fushman, Dina ^b   Yu, Hong ^c   Cohen, Kevin B ^d  

^a CNRS   (France)

^b NATIONAL INSTITUTES OF HEALTH   (United States)

^c UNIVERSITY OF WISCONSIN MILWAUKEE   (United States)

^d UNIVERSITY OF COLORADO SCHOOL OF MEDICINE   (United States)

Author keywords

Evaluation; Image mining; Information extraction; Literature based discovery; Natural language processing; Question answering; Text mining; Text summarization; User orientation

Indexed keywords

BIOMEDICINE; DATA BASE; INFORMATION PROCESSING; INFORMATION RETRIEVAL; INFORMATION STORAGE; KNOWLEDGE DISCOVERY; MACHINE LEARNING; MEDICAL LITERATURE; MEDICAL RESEARCH; METHODOLOGY; NATURAL LANGUAGE PROCESSING; QUESTIONNAIRE; REVIEW; TAXONOMIC IDENTIFICATION;

ABSTRACTING AND INDEXING AS TOPIC; ARTIFICIAL INTELLIGENCE; BIOLOGY; DATABASES, BIBLIOGRAPHIC; FORECASTING; NATURAL LANGUAGE PROCESSING; PERIODICALS AS TOPIC; VOCABULARY, CONTROLLED;

EID: 36348990139     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbm045     Document Type: Review

References (134)

1. Aronson AR, Bodenreider 0, Demner-Fushman D, et al. From indexing the biomedical literature to coding clinical text: Experience with MTI and machine learning approaches. In: Biological, Translational, and Clinical Language Processing, Prague, Czech Republic: Association for Computational Linguistics, 2007;105-12.
2. S Hunter L, Cohen KB. Biomedical language processing: What's beyond PubMed? Mol Cell 2006;21:589-94.
3. Ng SK. Integrating text mining with data mining. In: Ananiadou S, McNaught J, (eds). Text Mining for Biology and Biomedicine. Norwood, Massachussets: Artech House Publishers, 2006.
4. Baumgartner Jr. WA, Cohen KB, Fox L, et al. Manual curation is not sufficient for annotation of genomic databases. Bioinformatics (ISMB proceedings issue) 2007;23(13): I4l-i48.
5. S Cohen AM, Hersh WA. Survey of current work in biomedical text mining. Brief Bioinform 2005;6(1):57-71.
6. S Spasic I, Anamadou S, McNaught J, et al. Text mining and ontologies in biomedicine: Making sense of raw text. Brief Bioinform 2005;6(3):239-51.
7. S Ananiadou S, Kell DBB, Tsujii JII. Text mining and its potential applications in systems biology. Trends Biotechnol 2006;24 12):571-579.
8. S de Bruip B, Martin J. Getting to the (c)ore of knowledge: Mining biomedical literature. Int J Med Inform 2002;67:7-18.
9. S Cohen KB, Hunter L. Natural language processing and systems biology. In: Dubitzky W, Azuaje F, (eds). Artificial Intelligence Methods and Tools for Systems Biology. Heidelberg: Springer, 2004;147-74.
10. S Weeber M, Kors JA, Mons B. Online tools to support literature-based discovery in the life sciences. Brief Bioinform 2005;6(3):277-86.
11. S Shatkay H. Hairpins in bookstacks: Information retrieval from biomedical text. Brief Bioinform 2005;6(3):222-38.
12. S Krallinger M, Valencia A. Text-mining and information-retrieval services for molecular biology. Genome Biol 2005; 6:224.
13. S Jensen LJ, Saric J, Bork P. Literature mining for the biologist: From information retrieval to biological discovery. Nat Rev Genet 2006;7:119-29.
14. S Ananiadou S, McNaught J. Text Mining for Biology and Biomedicine Norwood, Massachussets: Artech House Publishers, 2006.
15. S Shatkay H, Craven M. Biomedical Text Mining. Cambridge, Massachussets: MIT Press, 2007.
16. Jackson P, Moulinier I. Natural Language. Processing for Online Applications: Text Retrieval, Extraction, and Categorization. Amsterdam: John Benjamins Publishing Company, 2002.
17. Hearst MA. What is text mining? http://www.ischool. berkeley.edu/~hearst/ text-mining.html, (October 2003 date last accessed).
18. Fukuda K, Tamura A, Tsunoda T, et al. Toward information extraction: Identifying protein names from biological papers. In: Pac Symp Biocomput Maui, Hawaii, 1998:707-18.
19. McDonald R, Pereira F. Identifying gene and protein mentions in text using conditional random fields. BMC Bioinform 2005;6 (Suppl)(1:S6).
20. Jin Y, McDonald R, Lerman K, et al. Automated recognition of malignancy mentions in biomedical literature. BMC Bioinform 2006;7:492.
21. Yeh A, Morgan A, Colosimo M, et al. BioCreAtIvE task 1A: Gene mention finding evaluation. BMC Bioinform 2005; 6:(Suppl)1.
22. Olsson F, Eriksson G, Franzén K, et al. Notions of correctness when evaluating protein name taggers. In: Proceedings of the 19th international conference on computational linguistics (COLING 2002): Taipei, Taiwan, 2002:765-71.
23. Dingare S, Nissim M, Finkel J, et al. A system for identifying named entities in biomedical text: How results from two evaluations reflect on both the system and the evaluations: Conference papers. Comp Funct Genomics 2005; 6(1-2):77-85.
24. Sandler T, Schein A, Ungar L. Automatic term list generation for entity tagging. Bioinformatics 2006;22(6): 651-7.
25. Tanabe L, Thom L, Matten W, et al. SemCat: Semantically categorized entities for genomics. AMIA Annu Symp Proc. Washington, DC, 2006:754-8.
26. Tanabe L, Wilbur W. A priority model for named entities. In: BioNLP, 2006.
27. Maguitman AG, Rechtsteiner A, Verspoor K, et al. Large-scale testing of biblionie informatics using Pfam protein families. In: Pac Symp Biocomput 11. Maui, Hawaii, 2006:76-87. http://psb.stanford.edu/ psb-online/proceedings/psb06/maguitman.pdf.
28. Bunescu R, Mooney R, Ramani A, et al. Integrating co-occurrence statistics with information extraction for robust retrieval of protein interactions from MEDLINE. In: BioNLP, 2006.
29. Curran JR, Moens M. Scaling context space. In: Proceedings of the 38th Annual Meeting of the Association for Computational Linquistics, Philadelphia PA. ACL, 2002:231-8.
30. Fundel K, Küffner R, Zimmer R. RelEx - relation extraction using dependency parse trees. Bioinformatics 2007;23:365-71. http://bioinformatics.oxfordjournals.org/cgi/content/full/23/3/365.
31. Hanisch D, Fundel K, Mevissen HT, et al. Prominer: Rule-based protein and gene entity recognition. BMC Bioinforma. 2005;6 Suppl 1):(S14).
32. Nédellec C. Learning language in logic - genic interaction extraction challenge. In: Proceedings of the ICML05 workshop: Learning Language in Logic (LLL05). Bonn, Germany, 2005.
33. Rinaldi F, Schneider G, Kaljurand K, et al. Mining of relations between proteins over biomedical scientific literature using a deep-linguistic approach. Artif Intell Med 2007;39 (2):127-36.
34. Rinaldi F, Schneider G, Kaljurand K, et al. An environment for relation mining over richly annotated corpora: The case of GENIA. BMC Bioinforma 2006;7(Suppl 3):(S3). http://www.biomedcentral.com/ concent/pdf/1471-2105-7-S3-S3.pdf
35. Miyao Y, Ohta T, Masuda K, et al. Semantic retrieval for the accurate identification of relational concepts in massive Lextbases. In: Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics (COLING/ACL 2006), Sydney, Australia. 2006:1017-24.
36. Ninomiya T, Tsuruoka Y, Miyao Y, et al. Fast and scalable HPSG parsing. TAL 2005 2007;46(2).
37. Ohta T, Miyao Y, Ninomiya T, et al. An intelligent search engine and GUI-based efficient MEDLINE search tool based on deep syntactic parsing. In: Proceedings of the 21st International Conference on Computational Linguistics and 44th A nnual Meeting of the Association for Computational Linguistics (COLING/ACL 2006), Interactive Presentation Sessions, Sydney, Australia. 2006:17-20.
38. Tsai TH, Chou WC, Lin YC, et al. BIOSMILE: Adapting semantic role labeling for biomedical verbs: An exponential model coupled with automatically generated template features. In: BioNLP, 2006.
39. Kim JD, Ohta T, Tateisi Y, et al. Genia corpus - a semantically annotated corpus for bio-textmining. Bioinformatics 2003;19 Suppl 1):180-2.
40. Masseroli M, Kilicoglu H, Lang FM, et al. Argument-predicate distance as a filter for enhancing precision in extracting predications on the genetic etiology of disease. BMC Bioinform 2006;7 291). http://www.biomedcentral.com/content/pdf/1471-2105-7-291.pdf.
41. Rodriguez-Esteban R, Iossifov I, Rzhetsky A. Imitating manual curation of text-mined facts in biomedicine. PLoS Comput Biol 2006;2 9).
42. Lee LC, Horn F, Cohen FE. Automatic extraction of protein point mutations using a graph bigram association. PLoS Comput Biol 2007;3(2).
43. Chang DTH, Weng YZ, Lin JH, et al. Protemot: Prediction of protein binding sites with automatically extracted geometrical templates. Nucleic Acids Res 2006;34:W303-9. http://www.pubmedcentral.nih.gov/ picrender.fcgi?artid-1538868&blobtype=pdf.
44. Chun HW, Tsuruoka Y, Kim JD, et al. Extraction of gene-disease relations from Medline using domain dictionaries and machine learning. In: Pac Symp Biocomput 11. Maui, Hawaii, 2006:4-15. http://psb.stanford.edu/psb-online/proceedings/psb06/chun.pdf.
45. Lussier Y, Borlawsky T, Rappaport D, et al. PhenoGO: Assigning phenotypic context to Gene Ontology annotations with natural language processing. In: Pac Symp Biocomput 11. Maui, Hawaii, 2006:64-75. http://psb.stanford.edu/psb-online/proceedings/psb06/lussier.pdf.
46. Ahlers CB, Fiszman M, Demner-Fushman D, et al. Extracting semantic predications from MEDLINE citations for pharmacogenomics. In: Pac Symp Biocomput 12. Maui, Hawaii, 2007:209-20.
47. Baker CJO, Witte R. Mutation mining: A prospector's tale. J Inform Syst Front 2006;8(1):47-57.
48. Kim JJ, Zhang Z, Park JC, et al. BioContrasts: Extracting and exploiting protein-protein contrastive relations from biomedical literature. Bioinformatics 2006;22:597-605. http://bioinformatics.oxfordjournals.org/cgi/reprint/22/5/597.pdf.
49. DUC 2006: Task, documents, and measures. http://duc.nist.gov/duc2006/ tasks.html (September 2007, date last accessed).
50. Demner-Fushman D, Lin J., Answer extraction, semantic clustering and extractive summarization for clinical question answering. In: Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics (COLING/ACL 2006), Sydney, Australia. 2006:841-8.
51. Ling X, Jiang J, He X, et al. Automatically generating gene summaries from biomedical literature. In: Pac Symp Biocomput 11. Maui, Hawaii, 2006:40-51. http://psb.stanford.edu/psb-online/proccedings/ psb06/ling.pdf.
52. Hersh W, Bhupatiraju RT. TREC Genomics track overview. In: The twelfth Text Retrieval Conference, TREC 2003. National Institute of Standards and Technology. Gaithersburg, Maryland, 2003:14-23.
53. Lu Z, Cohen KB, Hunter L. Finding GeneRIFs via Gene Ontology annotations. In: Pac Symp Biocomput 11. Maui, Hawaii, 2006:52-63. http://psb.stanford.edu/psb-online/proceedings/psb06/lu.pdf.
54. Rebholz-Schuhmann D, Kirsch H, Arregui M, et al. EBIMed-text crunching to gather facts for proteins from Medline, Bioinformatics 2007;23:e237-44.
55. Chiang JH, Shin JW, Liu HH, et al. GeneLibrarian: An effective gene-information summarization and visualization system. BMC Bioinform 2006;7(392).
56. Fernández J, Hoffmann R, Valencia A. iHOP Web services. Nucleic Acids Res 2007;35(Web Server issue)(W21-6).
57. Tanabe L, Scherf U, Smith LH, et al. Medminer: An Internet text-mining tool for biomedical information, with application to gene expression profiling. BioTechniques 1999; 27(6):1210-4, 1216-7.
58. Chen H, Sharp BM. Content-rich biological network constructed by mining PubMed abstracts. BMC Bioinform 2004;5(147).
59. Névéol A, Shooshan SE, Humphrey SM, et al. Multiple approaches to fine-grained indexing of the biomedical literature. In: Pac Symp Biocomput 12. Maui, Hawan, 2007:292-303.
60. Stoica E, Hearst M. Predicting gene functions from text using a cross-species approach. In: Pacific Symposium on Biocomputing 11, 2006:88-99. http://psb.stanford.edu/psb-online/proceedings/psb06/ stoica.pdf. PSB 2006: Maui, Hawaii.
61. Fyshe A, Szafron D. Term generalization and synonym resolution for biological abstracts: Using the gene ontology as a source of expert knowledge. In: BioNLP, 2006.
62. Höglund A, Blum T, Brady S, et al. Significantly improved prediction of subcellular localization by integrating text and protein sequence data. In: Pac Symp Biocomput 11. Maui, Hawaii, 2006:16-27. http://psb.stanford.edu/psb-online/proceedings/psb06/ hoglund.pdf.
63. Murphy RF, Velliste M, Yao J, et al. Searching online journals for fluorescence microscope images depicting protein subcellular location patterns. In: IEEE International Symposium on Bioinformatics and Biomedical Engineering, Rockville, Maryland, USA. 2001:119-28.
64. Ken Z, Cohen W, Murphy R. Extracting information from text and images for location proteomics. In: ACM SIGKDD Workshop on Data Mining in Bioinformatics (BIOKDD). Washington, DC, 2003:2-9.
65. Shatkay H, Chen N, Blostein D. Integrating image data into biomedical text categorization. Bioinformatics 2006;22: E446-53.
66. Yu H, Lee M. Accessing bioscience images from abstract sentences. Bioinformatics 2006;22:e547-56.
67. Rafkind B, Lee M, Chang S, et al. Exploring text and image features to classify images in bioscience literature. In: BioNLP, New York, USA. 2006:73-80.
68. Kou Z, Cohen W, Murphy R. A stacked graphical model for associating sub-images with sub-captions. In: Pacific Symposium on Biocomputing 12, 2007:257-68.
69. Rhodes J, Boyer S, Kreulen J, et al. Mining patents using molecular similarity search. In: Pac Symp Biocomput 12. Maui, Hawaii, 2007:304-15.
70. Claudinot S, Nicolas M, Oshima H, et al. Long-term renewal of hair follicles from clonogenic multipotent stem cells. PNAS 2005;102 41):14677-82. Image on cover page.
71. Tan SL, Nakao H, He Y, et al. NS5A, a nonstructural protein of hepatitis C virus, binds growth factor receptor-bound protein 2 adaptor protein in a Src homology 3 domain/ligand-dependent manner and perturbs mitogenic signaling. PNAS 1999;96(10):5533-8.
72. Schuler B, Lipman E, Steinbach P, et al. Polyproline and the "spectroscopic ruler" revisited with single-molecule fluorescence. PNAS2005;102(8):2754-9.
73. Kihara D, Lu H, Kolinski A, et al. TOUCHSTONE: An ab initio protein structure prediction method that uses threading-based tertiary restraints. PNAS 2001;98(18): 10125-30.
74. Hu H, Li M, Labrador J, et al. Cross GTPase-activating protein (CrossGAP)/Vilse links the Roundabout receptor to Rac to regulate midline repulsion. PNAS 2005;102(12): 4613-8.
75. Yu H. Towards answering biological questions with experimental evidence: Automatically identifying text that summarize image content in full-text articles. In: AMIA Annu Symp Proc. Washington, DC, 2006: 834-8.
76. Muller H, Deselaers T, Lehmann T, et al. Overview of the ImageCLEFmed 2006 medical retrieval and annotation tasks. In: CLEF 2006 Working Notes Alicante, Spain. 2006.
77. Lacoste C, Chevallet JP, Lim J, et al. IPAL knowledge-based medical image retrieval in ImageCLEFmed 2006. In: CLEF 2006 Working Notes, Alicante, Spain. 2006.
78. Voorhees EM, Tice DM. The TREC-8 question answering track evaluation. In: Voorhees EM, Harman D, (eds). Proceedings of the Eigth Text REtrieval Conference (TREC-8). NIST, 2000. Gaithersburg, Maryland, 2000.
79. Mollá D, Vicedo J. Question answering in restricted domains: An overview. Comput Linguist 2007;33(1):41-61.
80. Hersh W, Cohen AM, Roberts P, et al. TREC 2006 genomics track overview. In: The Fifteenth Text Retrieval Conference - TREC 2006. NIST. Gaithersburg, Maryland, 2006.
81. Zweigenbaum P. Question answering in biomedicine. In: De Rijke M, Webber B, (eds). Proc Workshop on Natural Language Processing for Question Answering, EACL 2003 Budapest. ACL, 2003:1-4.
82. Ely J, Osheroff J, Chambliss M, et al. Answering physicians' clinical questions: Obstacles and potential solutions. J Am Med Inform Assoc 2005;12:217-24.
83. Jacquemart P, Zweigenbaum P. Towards a medical question-answering system: A feasibility study. In: Stud Health Technol Inform, Vol. 95. Amsterdam: IOS Press, 2003:463-8.
84. Huang X, Lin J, Demner-Fushman D. Evaluation of PICO as a knowledge representation for clinical questions. In: AMIA Amin Symp Proc. Washington, DC, 2006:359-63.
85. Demner-Fushman D, Lin J. Answering clinical questions with knowledge-based and statistical techniques. Comput Linguist 2007;33:63-103.
86. Lin J, Demner-Fushman D. The role of knowledge in conceptual retrieval: A study in the domain of clinical medicine. In: 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Seattle, Washington, 2006:469-76.
87. Yu H, Lee M, Kaufman D, et al. Development, implementation, and a cognitive evaluation of a definitional question answering system for physicians. J Biomed Inform 2007;40(3):236-51.
88. Zhou W, Yu C, Torvik V, et al. A concept-based framework for passage retrieval in Genomics. In: Proceedings of Fifteenth Text REtrieval Conference, Gaithersbuig, 2006.
89. Demner-Fushman D, Humphrey S, Ide N, et al. Finding relevant passages in scientific articles: Fusion of automatic approaches vs. an interactive team effort. In: Proceedings Fifteenth Text REtrieval Conerence, Gaithersburg. 2006.
90. Jiang J, He X, Zhai C. Robust pseudo feedback estimation and HMM passage extraction: UIUC at TREC 2006 Genomics Track. In: Proceedings of Fifteenth Text REtrieval Conference, Gaithersburg. 2006.
91. Caporaso J, Baumgartner W, Kim H, et al. Concept recognition, information retrieval, and machine learning in genomics question-answering. In: Proceedings of Fifteenth Text REtrieval Conference, Gaithersburg. 2006.
92. Divoli A, Hearst M, Nakov P, et al. BioText team report for the TREC 2006 Genomics Track. In: Proceedings of Fifteenth Text REtrieval Conference, Gaithersburg, 2006.
93. Zheng H, Lin C, Huang L, et al. Using profile marching and text categorization for answer extraction in TREC Genomics. In: Proceedings of Fifteenth Text REtrieval Conference, Gaithersburg. 2006
94. Swanson DR. Fish oil, Raynaud's syndrome, and undiscovered public knowledge. Perspect Biol Med 1986;30:7-18.
95. Yetisgen-Yildiz M, Pratt W. Using statistical and knowledge-based approaches for literature-based discovery. J Biomed Inform 2006; 39(6):600-11.
96. Jelier R, Jenster G, Dorssers LC, et al. Text-derived concept profiles support assessment of DNA microarray data for acute myeloid leukemia and for androgen receptor stimulation. BMC Bioinform 2007;8:14.
97. Seki K, Mostafa J. Discovering implicit associations between genes and hereditary diseases. In: Pac Symp Biocomput12. Maui, Hawaii, 2007:316-27.
98. Pospisil P, Iyer LK, Adelstein SJ, et al. A combined approach to data mining of textual and structured data to identify cancer-related targets. BMC Bioinform 2006;7(354).
99. Palakal M, Bright J, Sebastian T, et al. A comparative study of cells in inflammation, EAE and MS using biomedical literature data mining. J Biomed Sci 2007;14(1):67-85.
100. Rzhetsky A, Iossifov I, Loh JM, et al. Microparadigms: Chains of collective reasoning in publications about molecular interactions. PNAS 2006;103:4940-5.
101. Hristovski D, Friedman C, Rindflesch T, et al. Exploiting semantic relations for literature-based discovery, AMIA Annu Symp Proc. Washington, DC, 2006:349-53.
102. Demaine J, Martin J, Wei L, et al. LitMiner: Integration of library services within a bio-informatics application. Biomed Digit Libr 2006;3(11), doi:10.1186/1742-5581-3-11, http://www.bio-diglib.com/content/3/1/11, http://www.litminer.com/.
103. Xiang Z, Zheng W, He Y. BBP: Brucella genome annotation with literature mining and curation. BMC Bioinform 2006;7(347).
104. Smalheiser NR, Torvik VI, Bischoff-Grethe A, et al. Collaborative development of the Arrowsmith two node search interface designed for laboratory investigators. J Biomed Discov Collab 2006;1(8). http://www.j-biomed-discovery.com/content/1/1/8.
105. Aubry M, Monnier A, Chicault C, et al. Combining evidence, biomedical literature and statistical dependence: New insights for functional annotation of gene sets. BMC Bioinform 2006;7:241.
106. Firth JR. Papers in Linguistics, 1934-1951. London: Oxford University Press, 1957.
107. Habert B, Zweigenbaum P. Contextual acquisition of information categories: What has been done and what can be done automatically?. In: Nevin BE, Johnson SM, (eds). The Legacy of Zellig Harris: Language and information into the 21st Century, Mathematics and computability of language, Vol. 2 Amsterdam: John Benjamins, 2002:203-31.
108. Hristovski D, Peterlin B, Mitchell J, et al. Using literature-based discovery to identify disease candidate genes. Int J Med Inform 2005;74(2-4):289-98.
109. Rindflesch TC, Fiszman M. The interaction of domain knowledge and linguistic structure in natural language processing: Interpreting hypernymic propositions in biomedical text. J Biomed Inform 2003;36:462-77.
110. Turtle HR, Croft WB. Evaluation of an inference network-based retrieval model. ACM T Inform Syst 1991; 9(3):187-222.
111. Torvik VI, Smalheiser N. A quantitative model for linking two disparate sets of articles in MEDLINE. Bioinformatics 2007.
112. Yeh AS, Hirschman L, Morgan AA. Evaluation of text data mining for database curation: Lessons learned from the KDD Challenge Cup. Bioinformatics 2003;19(Suppl 1): I331-9.
113. Voorhees E, TREC: Improving information access through evaluation. Bulletin of the American Society for Information Science and Technology 2005;32(1). http://www.asis.org/Bulletin/Oct-05/voorhees.html.
114. Wilbur WJ, Rzhetsky A, Shatkay H. New directions in biomedical text annotation: Definitions, guidelines and corpus construction. BMC Bioinform 2006;25(356).
115. Pyysalo S, Ginter F, Heimonen J, et al. BioInfer: A corpus for information extraction in the biomedical domain. BMC Bioinform 2007;8(50).
116. Morgan AA, Wellner B, Colombe JB, et al. Evaluating the automatic mapping of human gene and protein mentions to unique identifiers. In: Pac Symp Biocomput 12. Maui, Hawaii, 2007:281-91.
117. Karamanis N, Lewin I, Sealy R, et al. Integrating natural language processing with Flybase curation. In: Pac Symp Biocomput 12. Maui, Hawaii, 2007:245-56.
118. Muller-HM, Kenny EE, Sternberg PW. Textpresso: An ontology-based information retrieval and extraction system for biological literature. PLoS Biol 2004;2(11): E309.
119. Hersh W, Cohen A, Yang J, et al. Trec 2005 genomics track overview. In: The Fourteenth Text Retrieval Conference - TREC 2005 Gaithersburg, Maryland, 2005.
120. Hirschman L, Yeh A, Blaschke C, et al. Overview of BioCreAtIvE: critical assessment of information extraction for biology. BMC Bioinform 2005;6.
121. Camon EB, Bartell DG, Dimmer EC, et al. An evaluation of GO annotation retrieval for BioCreAtIvE and GOA. BMC Bioinform 2005;6(Suppl 1):S17.
122. Wattarujeekrit T, Shah PK, Collier N. PASBio: Predicate-argument structures for event extraction in molecular biology. BMC Bioinform 2004;5:155.
123. Chou WC, Tsai RTH, Su YS, et al. A semi-automatic method for annotating a biomedical proposition bank. In: Proceedings of the workshop on frontiers in linguistically annotated corpora 2006. Association for Computational Linguistics. Sydney, Australia, 2006:5-12.
124. Cohen KB, Hunter L. A critical review of PASBio's argument structures for biomedical verbs. BMC Bioinform 2006;7(Suppl 3):S5.
125. Rosario B, Hearst MA. Classifying semantic relations in bioscience texts. In: Proceedings of ACL 2004, 2004:430-7.
126. Hirschman L, Colosimo M, Morgan A, et al. Over-view of BioCreative Task 1B: Normalized gene lists. BMC Bioinform 2005;6(Suppl 1):S11.
127. Cohen AM. Unsupervised gene/protein named entity normalization using automatically extracted dictionaries. In: Linking biological literature, ontologies and databases: Mining biological semantics. Association for Computational Linguistics. Detroit, Michigan, 2005:17-24.
128. Fang HR, Murphy K, Jin Y, et al. Human gene name normalization using text matching with automatically extracted synonym dictionaries. In: Linking natural language processing andTbiology: towards deeper biological literature analysis. Association for Computational Linguistics. Brooklyn, New York, 2006:41-8.
129. Chen H, Sharp BM. Content-rich biological network constructed by mining PubMed abstracts. BMC Bioinform 2004;5:1471-2105.
130. Shah PK, Jensen LJ, Bone S, et al. Extraction of transcript diversity from scientific literature. PLoS Comput Biol 2005; 1 1):67-73.
131. Horn F, Lau AL, Cohen FE. Automated extraction of mutation data from the literature: Application of MuteXt to G protein-coupled receptors and nuclear hormone receptors. Bioinformatics 2004;20 (4):557-68.
132. Zweigenbaurr. P, Demner-Fushman D, Yu H, et al. New frontiers in biomedical text mining. In: Proc Pac Symp Biocomput 12. Maui, Hawaii, 2007:205-8.
133. Hirschman L, Bourne P, Cohen KB, et al. Translating Biology: Text mining tools that work, 2007. http://psb.stanford.edu/cfp-nlp.html (September 2007, date last accessed).
134. Verspoor K, Cohen KB, Mani I, et al. Introduction to BioNLP'06. In: Linking natural language processing and biology: Towards deeper biological literature analysis. Association for Computational Linguistics. Brooklyn, New York, 2006:3-5.