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.
| Original language | English |
|---|---|
| Pages (from-to) | 82-89 |
| Number of pages | 8 |
| Journal | Current Proteomics |
| Volume | 7 |
| Issue number | 2 |
| DOIs | |
| State | Published - 2010 |
Keywords
- Disease-related gene
- F-statistic
- Sequence features
- Usage bias
