A Survey on Deep Networks Approaches in Prediction of Sequence-Based Protein–Protein Interactions

被引：2
作者：
Mewara B. [1 ]
Lalwani S. [1 ]
机构：
[1] Department of Computer Science and Engineering, Career Point University, Kota
来源：
SN Computer Science | / 3卷 / 4期
关键词：
Deep learning; Deep networks; Long short term memory; Protein–protein interactions; Recurrent neural network;
D O I：
10.1007/s42979-022-01197-8
中图分类号：
学科分类号：
摘要：
The prominence of protein–protein interactions (PPIs) in system biology with diverse biological procedures has become the topic to discuss because it acts as a fundamental part in predicting the protein function of the target protein and drug ability of molecules. Numerous researches have been published to predict PPIs computationally because they provide an alternative solution to laboratory trials and a cost-effective way of predicting the most likely set of interactions at the entire proteome scale. In recent computational methods, deep learning has become a buzzword with numerous scientific researches. This paper presents, for the first time, a comprehensive survey of sequence-based PPI prediction by three popular deep learning architectures i.e. deep neural networks, convolutional neural networks and recurrent neural networks and its variants. The thorough survey discussed herein carefully mined every possible information, can help the researchers to further explore the success in this area. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
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