siPRED: Predicting siRNA Efficacy Using Various Characteristic Methods
| Autor: | Yen-Wei Chu, Chi-Wei Chen, Wei-Jie Pan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
Correlation coefficient
Biophysics lcsh:Medicine Context (language use) Biochemistry Pattern Recognition Automated RNA interference Molecular cell biology Artificial Intelligence Linear regression Genetics Humans Gene Silencing RNA Small Interfering lcsh:Science Biology Physics Multidisciplinary Artificial neural network Sequence Analysis RNA Applied Mathematics lcsh:R Weighting Support vector machine Nucleic acids Pattern recognition (psychology) Computer Science RNA lcsh:Q Epigenetics Gene expression Biological system Information Technology Energy (signal processing) Algorithms Mathematics Software Research Article |
| Zdroj: | PLoS ONE PLoS ONE, Vol 6, Iss 11, p e27602 (2011) |
| ISSN: | 1932-6203 |
| Popis: | Small interfering RNA (siRNA) has been used widely to induce gene silencing in cells. To predict the efficacy of an siRNA with respect to inhibition of its target mRNA, we developed a two layer system, siPRED, which is based on various characteristic methods in the first layer and fusion mechanisms in the second layer. Characteristic methods were constructed by support vector regression from three categories of characteristics, namely sequence, features, and rules. Fusion mechanisms considered combinations of characteristic methods in different categories and were implemented by support vector regression and neural networks to yield integrated methods. In siPRED, the prediction of siRNA efficacy through integrated methods was better than through any method that utilized only a single method. Moreover, the weighting of each characteristic method in the context of integrated methods was established by genetic algorithms so that the effect of each characteristic method could be revealed. Using a validation dataset, siPRED performed better than other predictive systems that used the scoring method, neural networks, or linear regression. Finally, siPRED can be improved to achieve a correlation coefficient of 0.777 when the threshold of the whole stacking energy is ≥−34.6 kcal/mol. siPRED is freely available on the web at http://predictor.nchu.edu.tw/siPRED. |
| Databáze: | OpenAIRE |
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