Prediction of Disease-Related Genes Based on Hybrid Features

Title: Prediction of Disease-Related Genes Based on Hybrid Features

Volume: 7 Issue: 2

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

Affiliation:

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

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.

Cite this article as:

Li Mingxiao, Li Zhibin, Jiang Zhenran and Li Dandan, Prediction of Disease-Related Genes Based on Hybrid Features, Current Proteomics 2010; 7 (2) . https://dx.doi.org/10.2174/157016410791330525