Current Proteomics

Bernd Rehm  
Institute of Molecular BioSciences Massey University
Private Bag 11222
Palmerston North
New Zealand

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Prediction of Disease-Related Genes Based on Hybrid Features

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

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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Article Details

VOLUME: 7
ISSUE: 2
Year: 2010
Page: [82 - 89]
Pages: 8
DOI: 10.2174/157016410791330525
Price: $58