Deep Learning in the Study of Protein-Related Interactions

Cheng       Shi; Jiaxing       Chen; Xinyue       Kang; Guiling       Zhao; Xingzhen       Lao; Heng       Zheng
doi:10.2174/0929866526666190723114142
Abstract

Protein-related interaction prediction is critical to understanding life processes, biological functions, and mechanisms of drug action. Experimental methods used to determine proteinrelated 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, and 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 others. 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, Protein- drug interactions.
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DOI https://dx.doi.org/10.2174/0929866526666190723114142	Print ISSN 0929-8665
Publisher Name Bentham Science Publisher	Online ISSN 1875-5305
Protein & Peptide Letters

Deep Learning in the Study of Protein-Related Interactions

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