Abstract
Protein-Protein Interaction (PPI) is a network of protein interconnections which regulates most of the biological methods. A sound state of biota largely depends on synchronized interactions between protein molecules, and any aberrant interactions between protein molecules may lead to complications, including cervical leukemia, tuberculosis, and other neural disorders. In PPI investigation, a plethora of computational methods have been developed over the years to analyze and predict PPI conclusively; however, a majority of these techniques proved to be strenuous and expensive. Therefore, the need for faster, accurate, and critical analysis of PPI warrants the adoption of Machine Learning (ML) methods such as Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest Model (RFM). These classifiers are useful in PPI unfolding in terms of amino acid sequence data. The SVM classifier, in particular, is serviceable in solving a majority of complex classification problems producing robust results in a reasonable time frame. This publication summarizes some state-of-art SVM based PPI investigations and challenges incurred in the application of the SVM method.
Highlights
Proteins are macromolecules consisting of long strings of amino acid residues that perform several functions inside organisms, including replication of DNA, stimuli-response mechanism, and molecular transportation
A Protein-Protein Interaction (PPI) system is modelled as a graph G = (V, E), where V is the set of the protein vertices, and E is the set of the edges representing pairwise protein interactions
The modern time is witnessing an outbreak of high-quality genomic information that warrants the use of sound methods such as machine learning to address multifaceted problems in PPI study
Summary
Proteins are macromolecules consisting of long strings of amino acid residues that perform several functions inside organisms, including replication of DNA, stimuli-response mechanism, and molecular transportation. These signals bind to receptor proteins to reach the desired cell through a channel known as cell membranes These receptors are connected inside and outside of a cell, forming a signaling pathway between source and destination. Machine learning (ML) methods, including SVM, ANN, RFM, and deep-learning, deliver critical means for judicious prediction of PPIs based on the direct derivation of protein information from amino acid sequences. In this context, Xia et al [1] reviewed the adoption of computational methods in Genomic, structure, domain, and sequence-based approaches. We reviewed SVM’s performance based on the cluster, genome, domain and customized feature-encoding tool
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