Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review

Area of Science:

• Computational Biology
• Artificial Intelligence
• Bioinformatics

Background:

• Protein-Protein Interactions (PPIs) are fundamental to biological processes.
• Understanding PPIs is crucial for deciphering biological systems and developing therapeutics.
• Deep learning (AI) is increasingly applied to analyze complex biological data.

Purpose of the Study:

• To provide a comprehensive review of deep learning methodologies applied to PPI analysis.
• To critically assess novel developments in the field from 2021-2023.
• To serve as a reference for researchers navigating deep learning in PPI analysis.

Main Methods:

• Literature review of studies published between 2021 and 2023.
• Analysis of cutting-edge deep learning techniques used in PPI analysis.
• Synthesis of recent advancements and trends.

Main Results:

• Identified a proliferation of deep learning applications in PPI analysis.
• Highlighted key deep learning methodologies currently employed.
• Showcased the rapid evolution of the field.

Conclusions:

• Deep learning is a transformative tool in computational biology for PPI analysis.
• This review consolidates recent progress, aiding researchers in understanding current trends.
• The findings offer insights into the synergistic relationship between AI and PPI research.