Collective instance-level gene normalization on the IGN corpus

NCUIR > College of Electrical Engineering & Computer Science > Department of Computer Science and information Engineering > journal & Dissertation > Item 987654321/106490

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/106490

Title:	Collective instance-level gene normalization on the IGN corpus
Authors:	蔡宗翰;Dai, Hong-Jie;Wu, Johnny Chi-Yang;Tsai, Richard Tzong-Han
Contributors:	資訊電機學院資訊工程學系
Keywords:	Algorithms;Annotations;Artificial intelligence;Bioinformatics;Biological activity;Biological effects;Computational Biology - methods;Computer science;Data Mining - methods;Databases, Genetic;Genes;Humans;Molecular Sequence Annotation;Names;Natural language;Natural Language Processing;Nomenclature;Object recognition;Proteins;Software;Studies
Date:	2013-11-25
Issue Date:	2026-04-23 13:24:52 (UTC+8)
Publisher:	Public Library of Science;United States: Public Library of Science
Abstract:	摘要： A high proportion of life science researches are gene-oriented, in which scientists aim to investigate the roles that genes play in biological processes, and their involvement in biological mechanisms. As a result, gene names and their related information turn out to be one of the main objects of interest in biomedical literatures. While the capability of recognizing gene mentions has made significant progress, the results of recognition are still insufficient for direct use due to the ambiguity of gene names. Gene normalization (GN) goes beyond the recognition task by linking a gene mention to a database ID. Unlike most previous works, we approach GN on the instance-level and evaluate its overall performance on the recognition and normalization steps in abstracts and full texts. We release the first instance-level gene normalization (IGN) corpus in the BioC format, which includes annotations for the boundaries of all gene mentions and the corresponding IDs for human gene mentions. Species information, along with existing co-reference chains and full name/abbreviation pairs are also provided for each gene mention. Using the released corpus, we have designed a collective instance-level GN approach using not only the contextual information of each individual instance, but also the relations among instances and the inherent characteristics of full-text sections. Our experimental results show that our collective approach can achieve an F-score of 0.743. The proposed approach that exploits section characteristics in full-text articles can improve the F-scores of information lacking sections by up to 1.8%. In addition, using the proposed refinement process improved the F-score of gene mention recognition by 0.125 and that of GN by 0.03. Whereas current experimental results are limited to the human species, we seek to continue updating the annotations of the IGN corpus and observe how the proposed approach can be extended to other species. 其他題名： PLoS One 出版者： United States: Public Library of Science 出版日期： 2013-11-25 出處： PloS one, 2013-11, Vol.8 (11), p.e79517 資源來源： [Open Access] DOAJ 오픈액세스 저널 디렉토리 版權： COPYRIGHT 2013 Public Library of Science 版權： 2013 Dai et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/3.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 版權： 2013 Dai et al 2013 Dai et al 識別號： ISSN: 1932-6203 識別號： EISSN: 1932-6203 識別號： DOI: 10.1371/journal.pone.0079517 識別號： PMID: 24282506
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	17	View/Open

社群 sharing

Loading...