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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/7790

Title: A Semi-supervised Learning Approach to Disease Gene Prediction
Authors: Nguyen, Thanh Phuong
Ho, Tu Bao
Issue Date: 2007-11
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: IEEE International Conference on Bioinformatics and Biomedicine, 2007. BIBM 2007.
Start page: 423
End page: 428
DOI: 10.1109/BIBM.2007.30
Abstract: Discovering human disease-causing genes (disease genes in short) is one of the most challenging problems in bioinformatics and biomedicine, as most diseases are related in some way to our genes. Various methods have been proposed to exploit existing data sources for solving the problem. We aim to develop a novel method to predict disease genes that takes into account the imbalance between known disease genes and unknown disease genes. To this end, our method makes the best of semi-supervised learning, integrating data of human protein-protein interactions and various biological data extracted from multiple proteomic/genomic databases. Experimental evaluation shows high performance of our proposed method. Also, a considerable number of potential disease genes were discovered.
Rights: Copyright (C) 2007 IEEE. Reprinted from IEEE International Conference on Bioinformatics and Biomedicine, 2007. BIBM 2007. 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 JAIST'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.
URI: http://hdl.handle.net/10119/7790
Material Type: publisher
Appears in Collections:a11-1. 会議発表論文 (Conference Papers)

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