The Implementation of Regularized Markov Clustering with Pigeon Inspired Optimization Algorithm in Analyzing the SARS-CoV-2 (COVID-19) Protein Interaction Network

The Implementation of Regularized Markov Clustering with Pigeon Inspired Optimization Algorithm in Analyzing the SARS-CoV-2 (COVID-19) Protein Interaction Network

M. Syamsuddin Wisnubroto, Marsudi Siburian, Febri Dwi Irawati

Proteins interact with other proteins, DNA, and other molecules, forming large-scale protein interaction networks and for easy analysis, clustering methods are needed. Regularized Markov clustering algorithm is an improvement of MCL where operations on expansion are replaced by new operations that update the flow distributions of each node. But to reduce the weaknesses of the RMCL optimization, Pigeon Inspired Optimization Algorithm (PIO) is used to replace the inflation parameters. The simulation results of IPC SARS-Cov-2 (COVID-19) inflation parameters  get the result of 42 proteins as the center of the cluster and 8 protein pairs interacting with each other. Proteins of COVID-19 that interact with 20 or more proteins are ORF8, NSP13, NSP7, M, N, ORF9C, NSP8, and NSP1. Their interactions might be used as a target for drug research.

2020

protein-protein interaction, SARS-CoV-2 (CoVID19), pigeon inspired optimization, regularized markov clustering

10.24042/djm.v3i3.6822

Desimal

Universitas Islam Negeri Raden Intan Lampung

Mathematics, Applied mathematics. Quantitative methods