Application of Machine Learning Approaches for Protein-protein Interactions Prediction

Medicinal Chemistry
Mengying ZhangBing Niu

Abstract

Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed. SVM and RF are two commonly used methods. Nowadays, more researchers predict PPIs by combining more than two methods. This review presents the application of machine learning approaches in predicting PPI. Many examples of success in identification and prediction in the area of PPI prediction have been discussed, and the PPIs research is still in progress.

Citations

Dec 24, 2019·Circulation Journal : Official Journal of the Japanese Circulation Society·Giuditta BenincasaClaudio Napoli
Nov 8, 2019·Current Pharmaceutical Design·Yuan ZhangHongying Hou
Oct 31, 2019·European Heart Journal. Cardiovascular Pharmacotherapy·Claudio NapoliMarco Salvatore
Feb 23, 2020·Biochemistry. Biokhimii︠a︡·E V PoverennayaE A Ponomarenko
Jan 17, 2020·International Journal of Molecular Sciences·Zengyan XieKunxian Shu
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Jan 16, 2021·Journal of Chemical Information and Modeling·Anju SharmaPritish Kumar Varadwaj
Jan 1, 2019·Computational and Structural Biotechnology Journal·Bing NiuLinfeng Zheng
May 29, 2021·Frontiers in Microbiology·Padhmanand SudhakarSéverine Vermeire
Aug 14, 2020·ACS Combinatorial Science·Alexander T TaguchiNeal W Woodbury

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