Abstract
Protein-protein interactions (PPIs) are fundamental to cellular processes, and understanding these interactions is crucial for deciphering disease mechanisms and drug discovery. Traditionally, experimental methods have been used to study PPIs, but these methods are often time-consuming and expensive. Machine learning (ML) approaches have emerged as a powerful tool for predicting protein-protein interaction networks by analyzing large-scale omics data. This article explores the use of machine learning techniques in predicting PPIs, including supervised and unsupervised learning methods, graph-based approaches, and deep learning models. We also discuss the challenges, limitations, and future directions of applying machine learning to PPI prediction.
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