Computational Methods for Predicting ncRNA-protein Interactions

Medicinal Chemistry
Shao-Wu Zhang, Xiao-Nan Fan

Abstract

RNA-protein interactions (RPIs) play an important role in many cellular processes. In particular, noncoding RNA-protein interactions (ncRPIs) are involved in various gene regulations and human complex diseases. High-throughput experiments have provided a large number of valuable information about ncRPIs, but these experiments are expensive and timeconsuming. Therefore, some computational approaches have been developed to predict ncRPIs efficiently and effectively. In this work, we will describe the recent advance of predicting ncRPIs from the following aspects: i) the dataset construction; ii) the sequence and structural feature representation, and iii) the machine learning algorithm. The current methods have successfully predicted ncRPIs, but most of them trained and tested on the small benchmark datasets derived from ncRNA-protein complexes in PDB database. The generalization performance and robust of these existing methods need to be further improved. Concomitant with the large numbers of ncRPIs generated by high-throughput technologies, three future directions for predicting ncRPIs with machine learning should be paid attention. One direction is that how to effectively construct the negative sample set. Another is the selec...Continue Reading

Citations

Oct 23, 2019·Journal of Cellular Physiology·Rodrigo A da SilvaWillian F Zambuzzi
Feb 5, 2019·Briefings in Bioinformatics·Xin QiBairong Shen
Apr 24, 2020·PLoS Computational Biology·Junpeng ZhangThuc Duy Le
Jul 25, 2019·FASEB Journal : Official Publication of the Federation of American Societies for Experimental Biology·Wenyun ChenZheng Zhang
Apr 27, 2018·Briefings in Bioinformatics·Ivan V AntonovYulia A Medvedeva
Aug 28, 2018·BioMed Research International·Huaixiang ZhouLiu Yang
Dec 4, 2020·Non-coding RNA·Tanvir AlamSebastian Schmeier

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