Volume 4, Issue 1 (3-2016)                   SCJ 2016, 4(1): 74-83 | Back to browse issues page


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Vasighi Zaker A, Jalili S. Candidate disease gene prediction using One-Class classification . SCJ. 2016; 4 (1) :74-83
URL: http://scj.kashanu.ac.ir/article-1-236-en.html

1- Master student Tarbiat Modares University, Tarbiat Modares University, Jalal Ale Ahmad Highway, P.O.Box: 14115-111, Tehran, Iran , a.vasighi@modares.ac.ir
2- Associate Professor Tarbiat Modares University, Tarbiat Modares University, Jalal Ale Ahmad Highway, P.O.Box: 14115-111, Tehran, Iran
Abstract:   (1005 Views)

Abstract: In disease gene identification and classification, users are only interested in classifying one specific class, disease genes, without considering other classes (non-disease genes). This situation is referred to as one-class classification. Existing machine learning-based methods typically use known disease gene as positive training set and unknown genes as negative training set to build a classifier. Since there are not any non-disease gene set, in this paper we apply OCSVM (one-class support vector machines) method for one-class classification of genes to identify disease genes. Our experimental results show the superiority of our proposed method in terms of better precision, recall, and F1-measures than existing methods.

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Type of Study: Research | Subject: Special
Received: 2015/04/11 | Accepted: 2015/08/6 | Published: 2016/04/13

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