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
Current Proteomics
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.
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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
DOI https://dx.doi.org/10.2174/157016410791330525 |
Print ISSN 1570-1646 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-6247 |
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