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

By Qi Liu, Qian Xu, Vincent Wenchen Zheng, H. Xue, Z. Cao, Qiang Yang. 2010

85 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Molecular Biology, Computational Biology & Bioinformatics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. Yu Zhang
2. Qiang Yang

In Molecular Biology

1. Yu Zhang
2. Qiang Yang
3. Qian Xu
4. Wouter M. Kouw

In Computational Biology & Bioinformatics

1. Yu Zhang
2. Qiang Yang
3. Qian Xu

Citations in the corpus

• An Overview of Multi-task Learning (2018, methodology): “Applications of MTL in bioinformatics and health informatics include organism modeling [101], mechanism identification of response to therapeutic targets [102], cross-platform siRNA efficacy prediction [103], detection of causal genetic markers through association analysis of multiple”
• A Survey on Multi-Task Learning (2017, methodology): “In [165], a multi-task model to detect commonly useful features based on significant tests is proposed for cross-platform siRNA efficacy prediction.”
• A Survey of Transfer and Multitask Learning in Bioinformatics (2011, methodology): “[39] proposed a cross-platform model based on multi-task linear regression.”