Multi-task learning for cross-platform siRNA efficacy prediction: an in-silico study

BMC Bioinformatics

Research Article

Qi Liu¹  Zhiwei Cao²  Hong Xue³  Qian Xu⁴  Vincent W Zheng⁴  Qiang Yang⁴ 

[1] College of Life Science and Biotechnology, Tongji University, China;Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong;College of Life Science and Biotechnology, Tongji University, China;Shanghai Center for Bioinformation Technology, China;Department of Biochemistry, Hong Kong University of Science and Technology, Hong Kong;Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong;

关键词: Root Mean Square Error;    Support Vector Regression;    Multitask Learning;    siRNA Design;    Efficacy Prediction;   

DOI  :  10.1186/1471-2105-11-181

 received in 2009-10-21, accepted in 2010-04-10,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundGene silencing using exogenous small interfering RNAs (siRNAs) is now a widespread molecular tool for gene functional study and new-drug target identification. The key mechanism in this technique is to design efficient siRNAs that incorporated into the RNA-induced silencing complexes (RISC) to bind and interact with the mRNA targets to repress their translations to proteins. Although considerable progress has been made in the computational analysis of siRNA binding efficacy, few joint analysis of different RNAi experiments conducted under different experimental scenarios has been done in research so far, while the joint analysis is an important issue in cross-platform siRNA efficacy prediction. A collective analysis of RNAi mechanisms for different datasets and experimental conditions can often provide new clues on the design of potent siRNAs.ResultsAn elegant multi-task learning paradigm for cross-platform siRNA efficacy prediction is proposed. Experimental studies were performed on a large dataset of siRNA sequences which encompass several RNAi experiments recently conducted by different research groups. By using our multi-task learning method, the synergy among different experiments is exploited and an efficient multi-task predictor for siRNA efficacy prediction is obtained. The 19 most popular biological features for siRNA according to their jointly importance in multi-task learning were ranked. Furthermore, the hypothesis is validated out that the siRNA binding efficacy on different messenger RNAs(mRNAs) have different conditional distribution, thus the multi-task learning can be conducted by viewing tasks at an "mRNA"-level rather than at the "experiment"-level. Such distribution diversity derived from siRNAs bound to different mRNAs help indicate that the properties of target mRNA have important implications on the siRNA binding efficacy.ConclusionsThe knowledge gained from our study provides useful insights on how to analyze various cross-platform RNAi data for uncovering of their complex mechanism.

【 授权许可】

Unknown   
© Liu et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.