Annotated chemical patent corpus: A gold standard for text mining

PLoS ONE

Volumn 9, Issue 9, 2014, Pages

  Akhondi, Saber A ^a   Klenner, Alexander G ^b,f   Tyrchan, Christian ^c   Manchala, Anil K ^d   Boppana, Kiran ^d   Lowe, Daniel ^e   Zimmermann, Marc ^b   Jagarlapudi, Sarma A R P ^d   Sayle, Roger ^e   Kors, Jan A ^a   Muresan, Sorel ^c,g  

^a ERASMUS MEDICAL CENTER   (Netherlands)

^b FRAUNHOFER INSTITUTE FOR ALGORITHMS AND SCIENTIFIC COMPUTING SCAI   (Germany)

^c ASTRAZENECA R AND D   (Sweden)

^d GVK BIOSCIENCES PVT LTD   (India)

^e NextMove Software Ltd   (United Kingdom)

^f EUROPEAN PATENT OFFICE   (Netherlands)

^g Akzo Nobel Surface Chemistry AB   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

CHEMICAL COMPOUND; G PROTEIN COUPLED RECEPTOR; HYDROLASE; ION CHANNEL; OXIDOREDUCTASE; PHOSPHATASE; PHOSPHOTRANSFERASE; PROTEINASE; TRANSFERASE;

ARTICLE; CHEMICAL ANALYSIS; CHEMICAL DATABASE; DATA MINING; EXTRACTION; PATENT; PRACTICE GUIDELINE; STANDARDIZATION; SYNTAX SCORE; TEXT MINING; VALIDATION PROCESS; CONTROLLED VOCABULARY; INFORMATION PROCESSING; NATURAL LANGUAGE PROCESSING; QUALITY CONTROL; STANDARDS;

BENCHMARKING; DATA CURATION; DATA MINING; NATURAL LANGUAGE PROCESSING; PATENTS AS TOPIC; VOCABULARY, CONTROLLED;

EID: 84907493884     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0107477     Document Type: Article

References (36)

1. Muresan S, Petrov P, Southan C, Kjellberg MJ, Kogej T, et al. (2011) Making every SAR point count: the development of Chemistry Connect for the largescale integration of structure and bioactivity data. Drug Discov Today 16: 1019-1030.
2. Southan C, Boppana K, Jagarlapudi SA, Muresan S (2011) Analysis of in vitro bioactivity data extracted from drug discovery literature and patents: Ranking 1654 human protein targets by assayed compounds and molecular scaffolds. J Cheminform 3: 14.
3. Tyrchan C, Boström J, Giordanetto F, Winter J, Muresan S (2012) Exploiting Structural Information in Patent Specifications for Key Compound Prediction. J Chem Inf Model 52: 1480-1489.
4. Kolarik C, Hofmann-Apitius M, Zimmermann M, Fluck J (2007) Identification of new drug classification terms in textual resources. Bioinformatics 23: i264-272.
5. Klinger R, Kolarik C, Fluck J, Hofmann-Apitius M, Friedrich CM (2008) Detection of IUPAC and IUPAC-like chemical names. Bioinformatics 24: i268-276.
6. Zimmermann M, Fluck J, Thi LT, Kolarik C, Kumpf K, et al. (2005) Information extraction in the life sciences: perspectives for medicinal chemistry, pharmacology and toxicology. Curr Top Med Chem 5: 785-796.
7. European Patent Office. Available: http://www.epo.org/. Accessed 2014 Jan 1.
8. United States Patent and Trademark Office. Available: http://www.uspto.gov/. Accessed 2014 Jan 1.
9. World Intellectual Property Organization. Available: http://www.wipo.int/. Accessed 2014 Jan 1.
10. Tseng Y-H, Lin C-J, Lin Y-I (2007) Text mining techniques for patent analysis. Inf Process Manag 43: 1216-1247.
11. Jessop DM, Adams SE, Murray-Rust P (2011) Mining chemical information from open patents. J Cheminform 3: 40.
12. Kiss M, Nagy A, Vincze V, Almási A, Alexin Z, et al. (2012) A Manually Annotated Corpus of Pharmaceutical Patents. Text, Speech and Dialogue. Springer Berlin Heidelberg. pp. 135-142.
13. Vazquez M, Krallinger M, Leitner F, Valencia A (2011) Text mining for drugs and chemical compounds: methods, tools and applications. Mol Inform 30: 506-519.
14. Corbett P, Batchelor C, Teufel S (2007) Annotation of chemical named entities. Proceedings of the Workshop on BioNLP 2007 Biological, Translational, and Clinical Language Processing -BioNLP '07. Morristown, NJ, USA: Association for Computational Linguistics. p. 57,. Available: http://portal.acm.org/citation.cfm?doid=1572392.1572403. Accessed 2014 Jul 10.
15. Kim JD, Ohta T, Tateisi Y, Tsujii J (2003) GENIA corpus-semantically annotated corpus for bio-textmining. Bioinformatics 19 Suppl 1: i180-182.
16. Kulick S, Bies A, Liberman M, Mandel M, McDonald R, et al. (2004) Integrated annotation for biomedical information extraction. Proc. of the Human Language Technology Conference and the Annual Meeting of the North American Chapter of the Association for Computational Linguistics (HLT/ NAACL) pp. 61-68.
17. Kolárik C, Klinger R, Friedrich CM, Hofmann-Apitius M, Fluck J (2008) Chemical names: terminological resources and corpora annotation. Workshop on Building and evaluating resources for biomedical text mining.
18. Krallinger M, Leitner F, Rabal O, Vazquez M, Oyarzabal J, et al. Overview of the chemical compound and drug name recognition (CHEMDNER) task; 2013. BioCreative Challenge Evaluation Workshop. vol. 2. pp. 2.
19. Grego T, Pe?zik P, Couto FM, Rebholz-Schuhmann D (2009) Identification of chemical entities in patent documents. Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living: Springer. pp. 942-949.
20. Degtyarenko K, De Matos P, Ennis M, Hastings J, Zbinden M, et al. (2008) ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Res 36: D344-D350.
21. Tiago G, Catia P, Bastos Hugo P (2012) Chemical entity recognition and resolution to ChEBI. ISRN Bioinformatics 2012.
22. De Matos P, Alcántara R, Dekker A, Ennis M, Hastings J, et al. (2010) Chemical entities of biological interest: an update. Nucleic Acids Res 38: D249-D254.
23. GVK BIO Target Class Based Compound Database. Available: http://www.gvkbio.com/products-services/informatics-analytics/products/standalonedatabases/. Accessed 2014 Jan 1.
24. Sayle R, Xie PH, Muresan S (2012) Improved chemical text mining of patents with infinite dictionaries and automatic spelling correction. J Chem Inf Model 52: 51-62.
25. Akhondi SA, Kors JA, Muresan S (2012) Consistency of systematic chemical identifiers within and between small-molecule databases. J Cheminf 4: 35.
26. About IUPAC. Available: http://www.iupac.org/home/about.html/. Accessed 2014 Jan 1.
27. Weininger D (1988) SMILES, a chemical language and information system. Introduction to methodology and encoding rules. J Chem Inf Comput Sci 28: 31-36.
28. Heller S, McNaught A, Stein S, Tchekhovskoi D, Pletnev I (2013) InChI -the worldwide chemical structure identifier standard. J Cheminform 5: 7.
29. About the InChI Standard. Available: http://www.inchi-trust.org/about-theinchi-standard/. Accessed 2014 Jan 1.
30. Undefined (1978) CAS Registry System. J Chem Inf Model 18: 58-58.
31. CAS REGISTRY -The gold standard for chemical substance information. Available: http://www.cas.org/content/chemical-substances/. Accessed 2014 Jan 1.
32. Krushinski JH, Robertson DW, Wong DT (1991) 3-aryloxy-3-substituted propanamines. USPTO US5023269 A.
33. Villani FJ, Wong JK (1987) Antihistaminic 8-(halo)-substituted 6,11-dihydro-11-(4-piperidylidene)-5H-benzo[5,6]cyclohepta[1,2-b]pyridines. USPTO US4659716 A.
34. Lowe DM, Sayle RA (2013) LeadMine: A grammar and dictionary driven approach to chemical entity recognition. BioCreative Challenge Evaluation Workshop. vol. 2 pp. 35.
35. Stenetorp P, Pyysalo S, Topić G, Ohta T, Ananiadou S, et al. (2012) BRAT: a web-based tool for NLP-assisted text annotation. Association for Computational Linguistics. pp. 102-107.
36. Lewin I, Kafkas S, Rebholz-Schuhmann D (2012) Centroids: Gold standards with distributional variation. LREC. pp. 3894-3900.