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Current Proteomics

Editor-in-Chief

ISSN (Print): 1570-1646
ISSN (Online): 1875-6247

Prediction of Disease-Related Genes Based on Hybrid Features

Author(s): Mingxiao Li, Zhibin Li, Zhenran Jiang and Dandan Li

Volume 7, Issue 2, 2010

Page: [82 - 89] Pages: 8

DOI: 10.2174/157016410791330525

Price: $65

Abstract

Identifying the disease-related genes of important human diseases from genomics can provide valuable clues for the discovery of potential therapeutic targets. However, discovering the disease-related genes by traditional biological experiments methods is usually laborious and time-consuming. Therefore, it is necessary to develop a powerful computational approach to improve the effectiveness of disease-related gene identification. In this study, multiple sequence features of known disease-related genes in 62 kinds of diseases were extracted, and then the selected features were further optimized and analyzed for disease-related genes prediction. The leave-one-out cross-validation tests demonstrated that 55% of the disease-related genes can be ranked within the top 10 of the prediction results, which confirmed the reliability of this multi-feature fusion approach.

Keywords: Disease-related gene, sequence features, usage bias, F-statistic

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