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Deep Learning in the Study of Protein-Related Interactions: Review

Author(s):

Cheng Shi, Jiaxing Chen, Xinyue Kang, Guiling Zhao, Xingzhen Lao* and Heng Zheng   Pages 1 - 11 ( 11 )

Abstract:


Protein-related interaction prediction is critical to understanding life processes, biological functions, and mechanisms of drug action. Experimental methods used to determine protein-related interactions have always been costly and inefficient. In recent years, advances in biological and medical technology have provided us with explosive biological and physiological data, deep learning-based algorithms have shown great promise in extracting features and learning patterns from complex data. At present, deep learning in protein research has emerged. In this review, we provide an introductory overview of the deep neural network theory and its unique properties. Mainly focused on the application of this technology in protein-related interactions prediction over the past five years, including protein-protein interactions prediction, protein-RNA\DNA, Protein–drug interactions prediction, and so on. Finally, we discuss some of the challenges that deep learning currently faces.

Keywords:

Protein interactions, Protein-RNA/DNA interactions, Deep learning, Machine learning, Computational biology

Affiliation:

School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, P.R, School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, P.R, School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, P.R, School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, P.R, School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, P.R, School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, P.R



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