A Review on Efficient and Scalable Graph-Based Clustering Algorithms for Protein Complex Identification in PPI...

Network clustering is employed in bioinformatics and data mining studies to investigate the structural and functional properties of protein-protein interaction (PPI) networks. In multiple studies over the past two decades, network clustering has proven valuable for uncovering functional modules and elucidating the functions of previously undiscovered proteins. Protein complexes are vital cellular components that play a crucial role in generating biological activity. Experimental techniques have inherent limitations in inferring protein complexes. Given these constraints, numerous computational methods have emerged over the past decade for predicting protein complexes. Typically, these methods take the input PPI data and generate predicted protein complexes as output subnetworks. Most of these methods have shown encouraging outcomes in predicting protein complexes. Prediction is challenging for sparse, small, and overlapping complexes. New strategies should include explicit knowledge about the biological characteristics of proteins to increase performance. Furthermore, specific issues should be considered more effectively in the future while developing new complex prediction algorithms. The bioinformatics community has developed various techniques for clustering PPI networks, which we identified, analyzed, and compared in this paper. This review evaluates various graph clustering algorithms for protein complex identification, facilitating the benchmarking of existing methods, identifying limitations, motivating the development of novel computational tools, and ultimately improving biological insight and therapeutic progress. Through the assessment of strengths and limitations, researchers may develop efficient and scalable algorithms designed explicitly for biological data, integrating graph-based methodologies with machine learning and deep learning approaches. This study is an invaluable tool for new researchers in the area to recognize upcoming trends, including dynamic PPI networks and temporal complex identification.