Affiliation:
1. School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
Abstract
Deep learning, a potent branch of artificial intelligence, is steadily leaving its transformative imprint across multiple disciplines. Within computational biology, it is expediting progress in the understanding of Protein–Protein Interactions (PPIs), key components governing a wide array of biological functionalities. Hence, an in-depth exploration of PPIs is crucial for decoding the intricate biological system dynamics and unveiling potential avenues for therapeutic interventions. As the deployment of deep learning techniques in PPI analysis proliferates at an accelerated pace, there exists an immediate demand for an exhaustive review that encapsulates and critically assesses these novel developments. Addressing this requirement, this review offers a detailed analysis of the literature from 2021 to 2023, highlighting the cutting-edge deep learning methodologies harnessed for PPI analysis. Thus, this review stands as a crucial reference for researchers in the discipline, presenting an overview of the recent studies in the field. This consolidation helps elucidate the dynamic paradigm of PPI analysis, the evolution of deep learning techniques, and their interdependent dynamics. This scrutiny is expected to serve as a vital aid for researchers, both well-established and newcomers, assisting them in maneuvering the rapidly shifting terrain of deep learning applications in PPI analysis.
Funder
Generative Artificial Intelligence System Inc. (GAIS).
Subject
Chemistry (miscellaneous),Analytical Chemistry,Organic Chemistry,Physical and Theoretical Chemistry,Molecular Medicine,Drug Discovery,Pharmaceutical Science
Reference185 articles.
1. AI in health and medicine;Rajpurkar;Nat. Med.,2022
2. Understanding and creating art with AI: Review and outlook;Cetinic;ACM Trans. Multimed. Comput. Commun. Appl. (TOMM),2022
3. A comprehensive review of the COVID-19 pandemic and the role of IoT, drones, AI, blockchain, and 5G in managing its impact;Chamola;IEEE Access,2020
4. Generative adversarial network: An overview of theory and applications;Aggarwal;Int. J. Inf. Manag. Data Insights,2021
5. A survey on generative adversarial networks: Variants, applications, and training;Jabbar;ACM Comput. Surv. (CSUR),2021
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