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Extracting biomarker information applying natural language processing and machine learning

Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.14/124663

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Title: Extracting biomarker information applying natural language processing and machine learning
Related: International Conference on Bioinformatics and Biomedical Engineering (4th : 2010) (18 - 20 June 2010 : Chengdu, China)
Related: 2010 4th international conference on bioinformatics and biomedical engineering (iCBBE) : June 18-20, 2010 Chengdu, China, p.1-4
DOI: 10.1109/ICBBE.2010.5514717
Publisher: Piscataway, N.J : IEEE Computer Society
Date: 2010
Author/Creator: Islam, Md Tawhidul
Author/Creator: Shaikh, Mostafa
Author/Creator: Nayak, Abhaya
Author/Creator: Ranganathan, Shoba
Description: In this paper, we detail an approach to a very specific task of information extraction namely, extracting biomarker information in biomedical literature. Starting with the abstract of a given publication, we first identify the evaluative sentence(s) among other sentences by recognizing words and phrases in the text belonging to semantic categories of interest to bio-medical entities (i.e., semantic category recognition). For the entities like, protein, gene and disease, we determine whether the statement refers to biomarker relationship (i.e., assertion classification). Finally, we identify the biomarker relationship among the bio-medical entities (i.e., semantic relationship classification). The system, Biomarker Information Extraction Tool (BIET) implements Machine Learning-based biomarker extraction using support vector machines (SVM). The system is trained and tested on a corpus of oncology related PubMed/MEDLINE literatures hand-annotated with biomarker information. We investigate the effectiveness of different features for this task and examine the amount of training data needed to learn the biomarker relationship with the entities. Our system achieved an average F-score of 86% for the task of biomarker information extraction comparing to the human annotated dataset (i.e. gold standard) scores.
Description: 4 page(s)
Resource Type: conference paper
Organisation: Macquarie University. Dept. of Chemistry and Biomolecular Sciences

Identifier: http://hdl.handle.net/1959.14/124663
Identifier: ISBN:9781424447121
Identifier: ISSN:2151-7614
Identifier: mq-rm-2009011593
Language: eng
Rights: Copyright 2010 IEEE. Reprinted from 2010 4th international conference on bioinformatics and biomedical engineering (iCBBE) : June 18-20, 2010 Chengdu, China. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Macquarie University’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
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