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標題: siPRED: Predicting siRNA Efficacy Using Various Characteristic Methods
作者: Pan, Wei-Jie
Chen, Chi-Wei
Chu, Yen-Wei
關鍵字: artificial neural-network;small interfering rnas;functional sirnas;mammalian-cells;design;sequences;efficient;model;selection;features
Project: Plos One, Volume 6, Issue 11, Page(s) e27602.
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
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0027602
Appears in Collections:基因體暨生物資訊學研究所

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