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Combining evidence, specificity, and proximity towards the normalization of gene ontology terms in text

Authors:

S. Gaudan,

A. Jimeno Yepes,

V. Lee,

D. Rebholz-SchuhmannAuthors Info & Claims

EURASIP Journal on Bioinformatics and Systems Biology, Volume 2008

Article No.: 4, Pages 1 - 9

https://doi.org/10.1155/2008/342746

Published: 01 January 2008 Publication History

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Abstract

Structured information provided by manual annotation of proteins with Gene Ontology concepts represents a high-quality reliable data source for the research community. However, a limited scope of proteins is annotated due to the amount of human resources required to fully annotate each individual gene product from the literature. We introduce a novel method for automatic identification of GO terms in natural language text. The method takes into consideration several features: (1) the evidence for a GO term given by the words occurring in text, (2) the proximity between the words, and (3) the specificity of the GO terms based on their information content. The method has been evaluated on the BioCreAtIvE corpus and has been compared to current state of the art methods. The precision reached 0.34 at a recall of 0.34 for the identified terms at rank 1. In our analysis, we observe that the identification of GO terms in the "cellular component" subbranch of GO is more accurate than for terms from the other two subbranches. This observation is explained by the average number of words forming the terminology over the different subbranches.

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Cited By

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Kim MXu YZaiane OGoebel R(2015)Recognition of Patient-Related Named Entities in Noisy Tele-Health TextsACM Transactions on Intelligent Systems and Technology10.1145/26514446:4(1-23)Online publication date: 24-Jul-2015
https://dl.acm.org/doi/10.1145/2651444
Kim JTuan LRanka SKahveci TSingh M(2012)Hybrid pattern matching for complex ontology term recognitionProceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine10.1145/2382936.2382973(289-296)Online publication date: 7-Oct-2012
https://dl.acm.org/doi/10.1145/2382936.2382973
Kim MDou QZaiane OGoebel RZhang ABorodovsky MOzsoyoglu GMikler A(2010)Unsupervised mapping of sentences to biomedical concepts based on integrated information retrieval model and clusteringProceedings of the First ACM International Conference on Bioinformatics and Computational Biology10.1145/1854776.1854820(322-329)Online publication date: 2-Aug-2010
https://dl.acm.org/doi/10.1145/1854776.1854820
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Index Terms

Combining evidence, specificity, and proximity towards the normalization of gene ontology terms in text
1. Applied computing
  1. Document management and text processing
  2. Life and medical sciences
2. Computing methodologies

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cover image EURASIP Journal on Bioinformatics and Systems Biology

EURASIP Journal on Bioinformatics and Systems Biology Volume 2008, Issue

January 2008

46 pages

ISSN:1687-4145

EISSN:1687-4153

Issue’s Table of Contents

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Hindawi Limited

London, United Kingdom

Publication History

Accepted: 15 February 2008

Published: 01 January 2008

Received: 21 November 2007

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Kim MXu YZaiane OGoebel R(2015)Recognition of Patient-Related Named Entities in Noisy Tele-Health TextsACM Transactions on Intelligent Systems and Technology10.1145/26514446:4(1-23)Online publication date: 24-Jul-2015
https://dl.acm.org/doi/10.1145/2651444
Kim JTuan LRanka SKahveci TSingh M(2012)Hybrid pattern matching for complex ontology term recognitionProceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine10.1145/2382936.2382973(289-296)Online publication date: 7-Oct-2012
https://dl.acm.org/doi/10.1145/2382936.2382973
Kim MDou QZaiane OGoebel RZhang ABorodovsky MOzsoyoglu GMikler A(2010)Unsupervised mapping of sentences to biomedical concepts based on integrated information retrieval model and clusteringProceedings of the First ACM International Conference on Bioinformatics and Computational Biology10.1145/1854776.1854820(322-329)Online publication date: 2-Aug-2010
https://dl.acm.org/doi/10.1145/1854776.1854820
Brauer FHuber MHackenbroich GLeser UNaumann FBarczynski WRappa MJones PFreire JChakrabarti S(2010)Graph-based concept identification and disambiguation for enterprise searchProceedings of the 19th international conference on World wide web10.1145/1772690.1772709(171-180)Online publication date: 26-Apr-2010
https://dl.acm.org/doi/10.1145/1772690.1772709

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Abstract

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Cited By

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Improving disease gene prioritization using the semantic similarity of Gene Ontology terms

Bayesian assignment of gene ontology terms to gene expression experiments

Gene Ontology analysis in multiple gene clusters under multiple hypothesis testing framework

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