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A hybrid approach to finding phenotype candidates in genetic texts


Collier, Nigel; Tran, Mai-Vu; Le, Hoang-Quynh; Oellrich, Anika; Kawazoe, Ai; Hall-May, Martin; Rebholz-Schuhmann, Dietrich (2012). A hybrid approach to finding phenotype candidates in genetic texts. In: 24th International Conference on Computational Linguistics (COLING 2012), Mumbai, India, 10 December 2012 - 14 December 2012.

Abstract

Named entity recognition (NER) has been extensively studied for the names of genes and gene products but there are few proposed solutions for phenotypes. Phenotype terms are expected to play a key role in inferring gene function in complex heritable diseases but are intrinsically difficult to analyse due to their complex semantics and scale. In contrast to previous approaches we evaluate state-of-the-art techniques involving the fusion of machine learning on a rich feature set with evidence from extant domain knowledge-sources. The techniques are validated on two gold standard collections including a novel annotated collection of 112 abstracts derived from a systematic search of the Online Mendelian Inheritance of Man database for auto-immune diseases. Encouragingly the hybrid model outperforms a HMM, a CRF and a pure knowledge-based method to achieve an F1 of 77.07. Disagreement analysis points to further improvements on this emerging NE task. The annotated corpus and guidelines are available on request.

Abstract

Named entity recognition (NER) has been extensively studied for the names of genes and gene products but there are few proposed solutions for phenotypes. Phenotype terms are expected to play a key role in inferring gene function in complex heritable diseases but are intrinsically difficult to analyse due to their complex semantics and scale. In contrast to previous approaches we evaluate state-of-the-art techniques involving the fusion of machine learning on a rich feature set with evidence from extant domain knowledge-sources. The techniques are validated on two gold standard collections including a novel annotated collection of 112 abstracts derived from a systematic search of the Online Mendelian Inheritance of Man database for auto-immune diseases. Encouragingly the hybrid model outperforms a HMM, a CRF and a pure knowledge-based method to achieve an F1 of 77.07. Disagreement analysis points to further improvements on this emerging NE task. The annotated corpus and guidelines are available on request.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Uncontrolled Keywords:conditional random fields, biomedicine, machine learning, genetic disorders, text mining
Language:English
Event End Date:14 December 2012
Deposited On:11 Jan 2013 08:28
Last Modified:25 Nov 2018 08:25
OA Status:Green
Related URLs:http://www.coling2012-iitb.org/
Project Information:
  • : FunderFP7
  • : Grant ID301806
  • : Project TitlePHENOMINER - Semantic mining of phenotype associations from the biomedical literature

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