Gene Entity Recognition of Full Text Articles

Noll, Manuel; Lété, Jonathan; Meyer, Patrick

doi:10.1145/3121138.3121167

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Gene Entity Recognition of Full Text Articles

Noll, Manuel; Lété, Jonathan; Meyer, Patrick

2017 • In ICBBS '17 Proceedings of the 6th International Conference on Bioinformatics and Biomedical Science

Peer reviewed

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https://hdl.handle.net/2268/214701

DOI
10.1145/3121138.3121167

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Keywords :

bibliomics; NER; Machine Learning; Automated gene-name identification

Abstract :

[en] Biomedical scientific literature is an unexploited treasure. Due to the staggering number of publications it is literally intractable to gather manually all information. Automatized information extraction (IE) is therefore key. An important subtask is the recognition of names in the text as specific entities ( named entity recognition, NER). NER for genes in biomedical literature is a challenging task. This paper reports preliminary results for the identification of gene names in full text with the naive Bayes, support vector machine and the random forest algorithm, showing that there is no loss on performance compared to the gene NER restricted to abstracts.

Disciplines :

Life sciences: Multidisciplinary, general & others
Computer science

Author, co-author :

Noll, Manuel ; Université de Liège - ULiège > Département des sciences de la vie > Biologie des systèmes et bioinformatique

Lété, Jonathan ; Université de Liège - ULiège > Master bioch. & biol. mol. & cel., à fin.

Meyer, Patrick ; Université de Liège - ULiège > Département des sciences de la vie > Biologie des systèmes et bioinformatique

Language :

English

Title :

Gene Entity Recognition of Full Text Articles

Publication date :

June 2017

Event name :

ICBBS '17: 6th International Conference on Bioinformatics and Biomedical Science

Event date :

June 22 - 24, 2017

Audience :

International

Main work title :

ICBBS '17 Proceedings of the 6th International Conference on Bioinformatics and Biomedical Science

Publisher :

ACM, New York, United States

ISBN/EAN :

978-1-4503-5222-2

Pages :

162-167

Peer reviewed :

Peer reviewed

Additional URL :

https://dl.acm.org/citation.cfm?id=3121138

Funders :

F.R.S.-FNRS - Fonds de la Recherche Scientifique

Available on ORBi :

since 02 October 2017

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Bibliography

Bada M, Eckert M, Evans D, Garcia K, Shipley K, Sitnikov D, Baumgartner WA, Cohen KB, Verspoor K, Blake JA, Hunter LE. Concept annotation in the CRAFT corpus. BMC Bioinformatics. 2012;4:161.
Chen, C., Liaw, A., Breiman, L.: Using Random Forest to Learn Imbalanced Data. (2004)
Coates-Stephens, Sam. The Analysis and Acquisition of Proper Names for the Understanding of Free Text. Computers and the Humanities. 1992: 26.441-456, San Francisco: Morgan Kaufmann Publishers.
Collins, Michael. Ranking Algorithms for Named–Entity Extraction: Boosting and the Voted Perceptron. 2002. In Proc. Association for Computational Linguistics.
Fukuda et al. Toward information extraction: identifying protein names from biological papers.1998. Proceedings of the Pacific Symposium on Biocomputing (PSB98).
Grishman, Ralph; Sundheim, B. A Brief History. In Proc. International Conference on Computational Linguistics.1996. Message Understanding Conference – 6
Hahn U, Beisswanger E, Buyko E, Poprat M, Tomanek K, Wermter J. Semantic annotations for biology–a corpus development initiative at the Jena University Language & Information Engineering (JULIE) Lab. LREC 2008–Proceedings of the 6th International Conference on Language Resources and Evaluation. 2008. pp. 28–30.
Hunter L, Carpenter B, Tsai R, Dai HJ, Liu F, Chen Y, Sun C, Katrenko S, Adriaans P, Blaschke C, Torres R, Neves M, Nakov P. et al. Overview of BioCreative II gene mention recognition. Genome Biol. 2008;4(Suppl 2):S2.
Hirschman, L., Yeh, A., Blaschke, C. and Valencia, A. (2005), ‘Overview of BioCreAtIvE: critical assessment of information extraction for biology', BMC Bioinformatics, Vol. 6 (Suppl 1), p. S1.
Junfrei Qiu et al. 2016. A survey of machine learning for big data processing. EURASIP Journal on Advances in Signal Processing 2016:67
Kazama et al. 2002. Tuning support vector machines for biomedical named entity recognition BioMed '02 Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain - Volume 3
Kim JD, Ohta T, Tateisi Y, Tsujii J. GENIA corpus–semantically annotated corpus for bio-textmining. Bioinformatics. 2003;4(Suppl 1):i180–i182.
Kristina Toutanova, Dan Klein, Christopher Manning, and Yoram Singer. 2003. Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network. In Proceedings of HLT-NAACL 2003, pp. 252-259.
Kulick S, Bies A, Liberman M, Mandel M, McDonald R, Palmer M, Schein A, Ungar L. Integrated annotation for biomedical information extraction. HLT-NAACL 2004 Workshop: Biolink 2004, “Linking Biological Literature, Ontologies and Databases”. 2004. pp. 61–68
Leser U., Hakenberg J.,. What makes a gene name? Named entity recognition in the biomedical literature. Brief Bioinform. 2005. 6(4) 357-69.
Liu, Y., Loh, H. T., Sun, A.: Imbalanced text classification: A term weight approach. Expert Systems with Applications, 36, pp. 690–701, (2009)
MEDLINE. Citation statistics. 2015. https://www.nlm.nih.gov/bsd/licensee/2015_stats/2015_LO.h tml
Ratinov, L., & Roth, D. Design challenges and misconceptions in named entity recognition. In Proceedings of the Thirteenth Conference on Computational Natural Language Learning. 2009. pp. 147-155. Association for Computational Linguistics.