Predicting siRNA potency with random forests and support vector machines

BMC Genomics

Research

Liangjiang Wang¹  Caiyan Huang¹  Jack Y Yang² 

[1] Department of Genetics and Biochemistry, Clemson University, 29634, Clemson, SC, USA;School of Electrical and Computer Engineering, Purdue University, 47907, West Lafayette, Indiana, USA;Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indiana University Purdue University, 46202, Indianapolis, Indiana, USA;Center for Research in Biological Systems, University of California at San Diego, 92093-0043, La Jolla, California, USA;

关键词: Support Vector Machine;    Random Forest;    Support Vector Machine Classifier;    Antisense Strand;    Matthews Correlation Coefficient;   

DOI  :  10.1186/1471-2164-11-S3-S2

来源: Springer

【 摘 要 】

BackgroundShort interfering RNAs (siRNAs) can be used to knockdown gene expression in functional genomics. For a target gene of interest, many siRNA molecules may be designed, whereas their efficiency of expression inhibition often varies.ResultsTo facilitate gene functional studies, we have developed a new machine learning method to predict siRNA potency based on random forests and support vector machines. Since there were many potential sequence features, random forests were used to select the most relevant features affecting gene expression inhibition. Support vector machine classifiers were then constructed using the selected sequence features for predicting siRNA potency. Interestingly, gene expression inhibition is significantly affected by nucleotide dimer and trimer compositions of siRNA sequence.ConclusionsThe findings in this study should help design potent siRNAs for functional genomics, and might also provide further insights into the molecular mechanism of RNA interference.

【 授权许可】

CC BY   
© Wang et al; licensee BioMed Central Ltd. 2010