New challenges for biological text-mining in the next decade

Journal of Computer Science and Technology

Volumn 25, Issue 1, 2010, Pages 169-179

  Dai, Hong Jie ^a,b   Chang, Yen Ching ^a   Tzong Han Tsai, Richard ^c   Hsu, Wen Lian ^a,b  

^a INSTITUTE OF INFORMATION SCIENCE   (Taiwan)

^b NATIONAL TSING HUA UNIVERSITY   (Taiwan)

^c YUAN ZE UNIVERSITY   (Taiwan)

Author keywords

Bioinformatics database; Mining method and algorithm; Text mining

Indexed keywords

HARDWARE; SOFTWARE ENGINEERING;

BIOINFORMATICS DATABASE; BIOLOGICAL LITERATURES; FULL TEXT ANALYSIS; LIFE-SCIENCES; MINING METHOD AND ALGORITHMS; RELATION EXTRACTION; SCHOLARLY PUBLICATION; TEXT MINING;

DATA MINING;

EID: 75749095148     PISSN: 10009000     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11390-010-9313-5     Document Type: Article

References (98)

1. Kim J D et al. Introduction to the bio-entity recognition task at JNLPBA. In Proc. the International Workshop on Natural Language Processing in Biomedicine and Its Applications (JNLPBA2004), Geneva, Switzerland, Aug. 28-29, 2004, pp.70-75.
2. L Hirschman, et al. 2005 Overview of BioCreAtIvE: Critical assessment of information extraction for biology BMC Bioinformatics 6 Suppl. 1 1 10.1186/1471-2105-6-S1-S1 (Pubitemid 46437463)
3. M Krallinger, et al. 2008 Evaluation of text-mining systems for biology: Overview of the Second BioCreative community challenge Genome Biology 9 Suppl. 2 1 10.1186/gb-2008-9-s2-s1
4. Hearst M A. Untangling text data mining. In Proc. the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics, College Park, USA, June 20-26, 1999, pp.3-10.
5. U Hahn, et al. 2007 Text mining: Powering the database revolution Nature 448 7150 130 10.1038/448130b (Pubitemid 47067358)
6. Hearst M. What is text mining. 2003, http://people.ischool.berkeley.edu/ hearst/text-mining.html.
7. HJ Dai, et al. 2008 BIOSMILE web search: A web application for annotating biomedical entities and relations Nucl. Acids Res. 36 Web Sever Issue 390 398 10.1093/nar/gkn319
8. D Rebholz-Schuhmann, et al. 2008 Text processing through Web services: Calling Whatizit Bioinformatics 24 2 296 298 10.1093/bioinformatics/btm557
9. JM Fernández, et al. 2007 iHOP web services Nucl. Acids Res. 35 Web Server Issue 21 26 10.1093/nar/gkm298
10. Elsevier Article 2.0 Contest. http://article20.elsevier.com/contest/home. html, Accessed July, 2009.
11. The Elsevier Grand Challenge. http://www.elseviergrandchallenge.com/, Accessed November, 2009.
12. BioCreAtIvE II.5. http://www.biocreative.org/events/biocreative-ii5/ biocreative-ii5/, Accessed December, 2009.
13. Ananiadou S, Chruszcz J et al. The national ventre for text mining: Aims and objectives. In Proc. UKKDD2007, Kent, UK, April 25, 2007, pp.6-12.
14. RSC Project Prospect. http://www.projectprospect.org/.
15. M Seringhaus M Gerstein 2008 Manually structured digital abstracts: A scaffold for automatic text mining FEBS Letters 582 8 1170 10.1016/j.febslet. 2008.02.073
16. A Morgan, et al. 2008 Overview of BioCreative II gene normalization Genome Biology 9 Suppl. 2 3 10.1186/gb-2008-9-s2-s3
17. G Gonzalez, et al. 2007 Mining gene-disease relationships from biomedical literature: Weighting protein-protein interactions and connectivity measures Proc. the Pacific Symposium on Biocomputing 12 28 29 10.1142/9789812772435-0004
18. RTH Tsai P Lai, et al. 2009 HypertenGene: Extracting key hypertension genes from biomedical literature with position and automatically-generated template features BMC Bioinformatics 10 Suppl. 5 9 10.1186/1471-2105-10-S15-S9
19. AM Cohen WR Hersh 2005 A survey of current work in biomedical text mining Briefings in Bioinformatics 6 1 57 71 10.1093/bib/6.1.57 (Pubitemid 40528580)
20. L Smith, et al. 2008 Overview of BioCreative II gene mention recognition Genome Biology 9 Suppl. 2 2 10.1186/gb-2008-9-s2-s2
21. M Krallinger, et al. 2008 Overview of the protein-protein interaction annotation extraction task of BioCreative II Genome Biology 9 Suppl. 2 4 10.1186/gb-2008-9-s2-s4
22. Chinchor N. MUC-7 named entity task definition (Version 3.5). In Proc. the 7th Message Understanding Conference, 1997.
23. U Leser J Hakenberg 2005 What makes a gene name? Named entity recognition in the biomedical literature Briefings in Bioinformatics 6 4 357 369 10.1093/bib/6.4.357 (Pubitemid 43091941)
24. RAA Erhardt, et al. 2006 Status of text-mining techniques applied to biomedical text Drug Discovery Today 11 7/8 315 325 10.1016/j.drudis.2006.02.011
25. Liu H et al. A study of abbreviations in MEDLINE abstracts. In Proc. AMIA Annual Symposium, San Antonio, USA, Nov. 9-13, 2002, pp.464-468.
26. Tanabe L, Wilbur W J. Tagging gene and protein names in full text articles. In Proc. the ACL-02 Workshop on Natural Language Processing in the Biomedical Domain - Volume 3, Philadelphia, USA, July 11, 2002, pp.9-13.
27. L Tanabe WJ Wilbur 2002 Tagging gene and protein names in biomedical text Bioinformatics 18 8 1124 1132 10.1093/bioinformatics/18.8.1124
28. Zhao S. Named entity recognition in biomedical texts using an HMM model. In Proc. the International Joint Workshop on Natural Language Processing in Biomedicine and Its Applications, Geneva, Switzerland, Aug. 28-29, 2004, pp.84-87.
29. Kazama J i et al. Tuning support vector machines for biomedical named entity recognition. In Proc. the ACL-02 Workshop on Natural Language Processing in the Biomedical Domain - Volume 3, Philadelphia, USA, July 11, 2002, pp.1-8.
30. Finkel J et al. Exploiting context for biomedical entity recognition: From syntax to the web. In Proc. the International Joint Workshop on Natural Language Processing in Biomedicine and Its Applications, Geneva, Switzerland, Aug. 28-29, 2004, pp.88-91.
31. RTH Tsai, et al. 2006 NERBio: Using selected word conjunctions, term normalization, and global patterns to improve biomedical named entity recognition BMC Bioinformatics 7 Suppl. 5 11 10.1186/1471-2105-7-S5-S11 (Pubitemid 46443020)
32. Si L et al. Boosting performance of bio-entity recognition by combining results from multiple systems. In Proc. the 5th International Workshop on Bioinformatics, Chicago, USA, Aug. 21, 2005, pp.76-83.
33. R Altman, et al. 2008 Text mining for biology - The way forward: Opinions from leading scientists Genome Biology 9 Suppl. 2 7 10.1186/gb-2008-9-s2-s7
34. A Jimeno, et al. 2008 Assessment of disease named entity recognition on a corpus of annotated sentences BMC Bioinformatics 9 Suppl. 3 3 10.1186/1471-2105-9-S3-S3 (Pubitemid 351959577)
35. H Yu, et al. 2002 Mapping abbreviations to full forms in biomedical articles Journal of the American Medical Informatics Association 9 3 262 272 10.1197/jamia.M0913 (Pubitemid 34975820)
36. AS Schwartz MA Hearst 2003 A simple algorithm for identifying abbreviation definitions in biomedical text Proc. Pac. Symp. Biocomput. 8 451 462
37. R Podowski, et al. 2005 Suregene, a scalable system for automated term disambiguation of gene and protein names Journal of Bioinformatics and Computational Biology 3 3 743 770 10.1142/S0219720005001223 (Pubitemid 41015286)
38. L Hirschman, et al. 2005 Overview of BioCreAtIvE task 1B: Normalized gene lists BMC Bioinformatics 6 Suppl. 1 11 10.1186/1471-2105-6-S1-S11 (Pubitemid 46437508)
39. W Cohen E Minkov 2006 A graph-search framework for associating gene identifiers with documents BMC Bioinformatics 7 440 10.1186/1471-2105-7-440 (Pubitemid 44614262)
40. Leitner F. Comparative community assessments for applied biomedical text mining: BioCreative II challenge and metaservices. In Intelligent Systems for Molecular Biology (ISMB) and European Conference on Computational Biology (ECCB), Highlights Track, Stockholm, Sweden, June 27-July 2, 2009.
41. K Fundel D Guttler, et al. 2005 A simple approach for protein name identification: Prospects and limits BMC Bioinformatics 6 Suppl. 1 15 10.1186/1471-2105-6-S1-S15 (Pubitemid 46442122)
42. Hakenberg J et al. Me and my friends: Gene mention normalization with background knowledge. In Proc. the Second BioCreAtIvE Challenge Evaluation Workshop, Madrid, Spain, April 23-25, 2007, p.23-25.
43. K Seki M Javed 2007 Discovering implicit associations between genes and hereditary diseases Proc. Pac. Symp. Biocomput. 12 316 327 10.1142/ 9789812772435-0030
44. JW Cooper A Kershenbaum 2005 Discovery of protein-protein interactions using a combination of linguistic, statistical and graphical information BMC Bioinformatics 6 143 10.1186/1471-2105-6-143 (Pubitemid 41358004)
45. PK Shah, et al. 2003 Information extraction from full text scientific articles: Where are the keywords? BMC Bioinformatics 4 20 10.1186/1471-2105-4-20 (Pubitemid 38751250)
46. H Shatkay, et al. 2006 Integrating image data into biomedical text categorization Bioinformatics 22 14 446 453 10.1093/bioinformatics/btl235 (Pubitemid 44288315)
47. Z Kou, et al. 2007 A stacked graphical model for associating information from text and images in figures Proc. Pac. Symp. Biocomput. 12 257 268 10.1142/9789812772435-0025 2317242
48. J Saric, et al. 2006 Extraction of regulatory gene/protein networks from Medline Bioinformatics 22 6 645 650 10.1093/bioinformatics/bti597 2204048
49. T Ono, et al. 2001 Automated extraction of information on protein-protein interactions from the biological literature Bioinformatics 17 2 155 161 10.1093/bioinformatics/17.2.155
50. S Kim, et al. 2008 Kernel approaches for genic interaction extraction Bioinformatics 24 1 118 126 10.1093/bioinformatics/btm544
51. R Bunescu R Mooney 2006 Subsequence kernels for relation extraction Advances in Neural Information Processing Systems 18 171 178
52. T Barnickel, et al. 2009 Large scale application of neural network based semantic role labeling for automated relation extraction from biomedical texts PLoS One 4 7 6393 10.1371/journal.pone.0006393
53. A Ramani, et al. 2005 Consolidating the set of known human protein-protein interactions in preparation for large-scale mapping of the human interactome Genome Biology 6 5 40 10.1186/gb-2005-6-5-r40 2123278
54. R Bunescu, et al. 2005 Comparative experiments on learning information extractors for proteins and their interactions Artificial Intelligence in Medicine 33 2 139 155 10.1016/j.artmed.2004.07.016 (Pubitemid 40462453)
55. Rosario B, Hearst M A. Multi-way relation classification: Application to protein-protein interactions. In Proc. the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, Vancouver, Canada, Oct. 6-8, 2005, pp.732-739.
56. Craven M, Kumlien J. Constructing biological knowledge bases by extracting information from text sources. In Proc. the 7th International Conference on Intelligent Systems for Molecular Biology, Heidelberg, Germany, Aug. 6-10, 1999, pp.77-86.
57. TC Rindflesch, et al. 2000 EDGAR: Extraction of drugs, genes and relations from the biomedical literature Proc. Pac. Symp. Biocomput. 5 514 525
58. HW Chun, et al. 2006 Extraction of gene-disease relations from Medline using domain dictionaries and machine learning Proc. the Pacific Symposium on Biocomputing 11 4 15
59. Tsai R T H et al. HypertenGene: Extracting key hypertension genes from biomedical literature with position and automatically-generated template features. To appear in BMC Bioinformatics, 2009.
60. Y Miyao K Sagae, et al. 2008 Evaluating contributions of natural language parsers to protein-protein interaction extraction Bioinformatics 25 3 394 400 10.1093/bioinformatics/btn631
61. L Wong 2001 PIES, a protein interaction extraction system Proc. Pacific Symposium on Biocomputing 6 520 531
62. Castaño J et al. Anaphora resolution in biomedical literature. In International Symposium on Reference Resolution for NLP, Alicante, Spain, June 3-4, 2002.
63. Pustejovsky J et al. Medstract: Creating large-scale information servers for biomedical libraries. In Proc. the ACL-02 Workshop on Natural Language Processing in the Biomedical Domain, Philadelphia, USA, July 11, 2002, pp.85-92.
64. Nguyen N et al. Challenges in pronoun resolution system for biomedical text. In Proc. the Sixth International Language Resources and Evaluation (LREC2008), Marrakech, Morocco, May 28-30, 2008.
65. Tsai R T H et al. PubMed-EX: A web browser extension to enhance PubMed search with text mining features. Bioinformatics, 2009, [Epub ahead of print].
66. Z Zhang, et al. 2009 Bringing Web 2.0 to bioinformatics Brief Bioinform. 10 1 1 10 1154.70336 10.1093/bib/bbn041
67. Cheung K et al. Semantic Web Approach to Database Integration in the Life Sciences. Semantic Web: Revolutionizing Knowledge Discovery in the Life Sciences, Springer, 2007, pp.11-30.
68. R Dowell, et al. 2001 The distributed annotation system BMC Bioinformatics 2 7 10.1186/1471-2105-2-7 (Pubitemid 38751157)
69. O'Reilly T. What is Web 2.0: Design patterns and business models for the next generation of software. 2005, http://www.oreillynet.com/pub/a/oreilly/tim/ news/2005/09/30/what-is-web-20.html.
70. B Mons, et al. 2008 Calling on a million minds for community annotation in WikiProteins Genome Biology 9 5 89 10.1186/gb-2008-9-5-r89 (Pubitemid 351974341)
71. C Baral, et al. 2007 CBioC: Beyond a prototype for collaborative annotation of molecular interactions from the literature Proc. Computational Systems Bioinformatics Conference 6 381 384 10.1142/9781860948732-0038
72. K Oda, et al. 2008 New challenges for text mining: Mapping between text and manually curated pathways BMC Bioinformatics 9 Suppl. 3 5 10.1186/1471-2105-9-S3-S5 2369043 (Pubitemid 351959575)
73. M Kanehisa, et al. 2008 KEGG for linking genomes to life and the environment Nucleic Acids Research 36 Database Issue 480 484
74. Hirschman L, Blaschke C. Evaluation of Text Mining in Biology. Text Mining for Biology and Biomedicine, Artech House, 2005, pp.213-245.
75. A Yeh, et al. 2002 Background and overview for KDD Cup 2002 task 1: Information extraction from biomedical articles ACM SIGKDD Explorations Newsletter 4 2 87 89 10.1145/772862.772873
76. W Hersh E Voorhees 2009 TREC genomics special issue overview Information Retrieval 12 1 1 15 10.1007/s10791-008-9076-6
77. Hakenberg J, Plake C et al. LLL'05 challenge: Genic interaction extraction-identification of language patterns based on alignment and finite state automata. In Proc. the ICML05 Workshop: Learning Language in Logic (LLL05), 2005, 14: 38-45.
78. Kim J D et al. Overview of BioNLP'09 shared task on event extraction. In Proc. the BioNLP 2009 Workshop Companion Volume for Shared Task, Boulder, USA, June 4-5, 2009, pp.1-9.
79. JD Kim, et al. 2008 Corpus annotation for mining biomedical events from literature BMC Bioinformatics 9 10 10.1186/1471-2105-9-10
80. G Bader, et al. 2006 Pathguide: A pathway resource list Nucleic Acids Research 34 Database Issue 504 506 10.1093/nar/gkj126
81. E Camon, et al. 2004 The gene ontology annotation (GOA) database: Sharing knowledge in Uniprot with Gene Ontology Nucleic Acids Research 32 Database Issue 262 266 10.1093/nar/gkh021
82. JD Kim, et al. 2003 GENIA corpus-A semantically annotated corpus for bio-textmining Bioinformatics 19 Suppl. 1 180 182 10.1093/bioinformatics/btg1023 (Pubitemid 41185616)
83. L Tanabe, et al. 2005 GENETAG: A tagged corpus for gene/protein named entity recognition BMC Bioinformatics 6 Suppl. 1 3 10.1186/1471-2105-6-S1-S3 (Pubitemid 46442951)
84. Heimonen J et al. Complex-to-pairwise mapping of biological relationships using a semantic network representation. In Proc. the Third International Symposium on Semantic Mining in Biomedicine (SMBM2008), Turku, Finland, Sept. 1-3, 2008, pp.45-52.
85. Rosario B, Hearst M A. Classifying semantic relations in bioscience texts. In Proc. the 42nd Annual Meeting on Association for Computational Linguistics, Barcelona, Spain, July 21-26, 2004, Article No. 43.
86. Berleant D et al. Corpus properties of protein interaction descriptions in MEDLINE. 2003, http://class.ee.iastate.edu/berleant/home/me/cv/papers/ corpuspropertiesstart.htm.
87. Nedellec C. Learning language in logic-genic interaction extraction challenge. In Proc. the ICML05 Workshop: Learning Language in Logic (LLL05), Bonn, Germany, Aug. 7, 2005, pp.31-37.
88. T Wattarujeekrit, et al. 2004 PASBio: Predicate-argument structures for event extraction in molecular biology BMC Bioinformatics 5 155 10.1186/1471-2105-5-155 (Pubitemid 40195353)
89. Chou W C et al. A semi-automatic method for annotating a biomedical proposition bank. In Proc. ACL Workshop on Frontiers in Linguistically Annotated Corpora, Sydney, Australia, July 22, 2006, pp.5-12.
90. Seth K et al. Integrated annotation for biomedical information extraction. In Proc. HLT/NAACL-2004, Boston, USA, May 2-7, 2004, pp.61-68.
91. Tateisi Y, Tsujii J. Part-of-speech annotation of biology research abstracts. In Proc. the 4th International Conference on Language Resource and Evaluation (LREC2004), Lisbon, Portugal, May 26-28, 2004, pp.1267-1270.
92. Tateisi Y et al. Syntax annotation for the GENIA corpus. In Proc. IJCNLP 2005, Companion Volume, Jeju Island, Korea, Oct. 11-13, 2005, pp.222-227.
93. Lease M, Charniak E. Parsing biomedical literature. In Proc. the Second International Joint Conference on Natural Language Processing, Jeju Island, Korea, Oct. 11-13, 2005, pp.58-69.
94. L Smith, et al. 2004 MedPost: A part-of-speech tagger for BioMedical text Bioinformatics 20 14 2320 2321 10.1093/bioinformatics/bth227 (Pubitemid 39341625)
95. Krallinger M et al. The BioCreative II.5 challenge overview. In Proc. the BioCreative II.5 Workshop 2009 on Digital Annotations, Madrid, Spain, Oct. 7-9, 2009, p.19.
96. GasperIn C et al. Annotation of anaphoric relations in biomedical full-text articles using a domain-relevant scheme. In Proc. the Discourse Anaphora and Anaphor Resolution Colloquium, Lagos (Algarve), Portugal, March 29-30, 2007, pp.19-24.
97. M McIntosh J Curran 2009 Challenges for automatically extracting molecular interactions from full-text articles BMC Bioinformatics 10 311 10.1186/1471-2105-10-311
98. KW Kohn 1999 Molecular interaction map of the mammalian cell cycle control and DNA repair systems Mol. Biol. Cell 10 8 2703 2734