Drug name recognition in biomedical texts: A machine-learning-based method

Drug Discovery Today

Volumn 19, Issue 5, 2014, Pages 610-617

  He, Linna ^a   Yang, Zhihao ^a   Lin, Hongfei ^a   Li, Yanpeng ^a  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

BIOINFORMATICS; COMPUTER PROGRAM; DATA EXTRACTION; DRUG INFORMATION; MACHINE LEARNING; PERFORMANCE; REVIEW; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BOOK; CLASSIFICATION; HUMAN; MEDICAL RESEARCH; PROCEDURES;

DRUG;

ARTIFICIAL INTELLIGENCE; BIOMEDICAL RESEARCH; HUMANS; PATTERN RECOGNITION, AUTOMATED; PHARMACEUTICAL PREPARATIONS; REFERENCE BOOKS, MEDICAL;

EID: 84901498101     PISSN: 13596446     EISSN: 18785832     Source Type: Journal    
DOI: 10.1016/j.drudis.2013.10.006     Document Type: Review

References (35)

1. R. Hale Text mining: getting more value from literature resources Drug Discov. Today 10 2005 377 379
2. I. Segura-Bedmar et al. Drug name recognition and classification in biomedical texts: a case study outlining approaches underpinning automated systems Drug Discov. Today 13 2008 816 823
3. Fickett, J. and Hayes, W. (2004) Text mining for drug discovery. European Pharmaceutical Contractor Available at: http://www.samedanltd.com/?mod= magazine&id=11&page=article&issid=12&pid=794&post, accessed October 2013
4. S.J. Zhao Named entity recognition in biomedical texts using an HMM model Proc. Joint Workshop Natural Language Process. Biomed. Appl. 2004 84 87
5. Y. Tsuruoka, and J. Tsujii Boosting precision and recall of dictionary-based protein name recognition Proc. ACL 2003 Workshop Natural Language Process. 2003 41 48
6. K. Yamamotoa et al. Use of morphological analysis in protein name recognition J. Biomed. Inf. 37 2004 471 482
7. Y. Tsuruokazy, and J. Tsujii Improving the performance of dictionary-based approaches in protein name recognition J. Biomed. Inf. 37 2004 461 470
8. T. Mitsumori et al. Gene/protein name recognition based on support vector machine using dictionary as features BMC Bioinformatics 6 Suppl. 1 2005 8
9. H. Liu et al. BioThesaurus: a web-based thesaurus of protein and gene names Bioinformatics 22 2006 103 105
10. U. Leser, and J. Hakenberg What makes a gene name? Named entity recognition in the biomedical literature Brief Bioinform. 6 2005 257 269
11. M. Torii et al. BioTagger-GM: a gene/protein name recognition system J. Am. Med. Inform. 16 2009 247 255
12. M. Yoneyama, and T. Fujita RNA recognition and signal transduction by RIG-I-like receptors Immunol. Rev. 227 2009 54 65
13. M. Campillos et al. Drug target identification using side-effect similarity Science 321 2008 263 266
14. O. Tuason et al. Biological nomenclatures: a source of lexical knowledge and ambiguity Pac. Symp. Biocomput. 2004 238 249
15. K. Fukuda et al. Toward information extraction: identifying protein names from biological papers Pac. Symp. Biocomput. 1998 707 718
16. E. Sang, and F.D. Meulder Introduction to the CoNLL-2003 shared task: language-independent named entity recognition Proc. Seventh Conf. Natural Language Learning HLT-NAACL 2003 142 147
17. D.M. Bikel et al. Nymble: a high-performance learning name-finder Proc. Fifth Conf. Appl. Natural Language Process. 1997 194 201
18. S.K. Saha et al. Feature selection techniques for maximum entropy based biomedical named entity recognition J. Biomed. Inf. 42 2009 905 911
19. D.C. Li et al. Conditional random fields and support vector machines for disorder named entity recognition in clinical texts Proc. Workshop Curr. Trends Biomed. Natural Language 2008 94 95
20. H. Isozaki, and H. Kazawa Efficient support vector classifiers for named entity recognition Proc. 19th Int. Conf. Comput. Linguistics 2002 1 7
21. Y.P. Li et al. Incorporating rich background knowledge for gene named entity classification and recognition BMC Bioinf. 10 2009 223
22. I. Segura-Bedmar et al. Semeval-2013 task 9: extraction of drug-drug interactions from biomedical texts Proc. Seventh International Workshop Semantic Evaluation 2013 341 350
23. I. Segura-Bedmar et al. A linguistic rule-based approach to extract drug-drug interactions from pharmacological documents in biomedical texts BMC Bioinf. 12 Suppl. 2 2011 1
24. C. Kolarik et al. Identification of new drug classification terms in textual resources Bioinformatics 23 2007 264 272
25. R. Klinger, and K. Tomanek Classical probabilistic Models and Conditional Random Fields 2007 Dortmund University of Technology Algorithm Engineering Report TR07-2-013
26. T. Rocktäschel et al. WBI-NER: the impact of domain-specific features on the performance of identifying and classifying mentions of drugs Proc. Seventh International Workshop Semantic Evaluation 2013 356 363
27. T. Rocktäschel et al. ChemSpot: a hybrid system for chemical named entity recognition Bioinformatics 28 2012 1633 1640
28. K. Hettne et al. A dictionary to identify small molecules and drugs in free text Bioinformatics 25 2009 2983 2991
29. P.P. Talukdar et al. A context pattern induction method for named entity extraction Proc. CoNLL 2006 2006 141 148
30. K.W. Church, and P. Hanks Word association norms, mutual information, and lexicography Compu. Linguist. 16 1989 22 29
31. A. Coulet et al. Using text to build semantic networks for pharmacogenomics J. Biomed. Inf. 43 2010 1009 1019
32. B. Settles ABNER: an open source tool for automatically tagging genes, proteins, and other entity names in text Bioinformatics 21 2005 3191 3192
33. J. Wilbur et al. BioCreative 2: gene mention task Proc. Second BioCreative Challenge Eval. Workshop 2007 7 16
34. D. Jessop et al. Oscar4: a exible architecture for chemical text-mining J. Chem. Inf. 3 2011 41
35. P. Corbett, and A. Copestake Cascaded classifiers for confidence-based chemical named entity recognition BMC Bioinf. 9 Suppl. 11 2008 4