Application of soft regularized markov clustering for analyzing protein-protein interaction in sars-cov-2 and other related coronavirus

S. A. Pratiwi, A. Bustamam, D. Sarwinda

Research output: Contribution to journalConference articlepeer-review

Abstract

Covid-19 is a global disease that has already infected people in the various parts of the world with increasing cases each day. So far, there has been around 20 million cases of Covid-19 that have occurred around the world. Furthermore, a lot of research has been conducted to overcome and cure this disease. One of the studies was uses protein-protein interactions (PPI) in Sars-Cov-2 and other coronavirus to analyze the interactions on the virus which can be used to find out more about how this virus interacts with each other. In this study, we used Markov Clustering (MCL) to analyze this virus. There are many variations of Markov Clustering that have been used in various studies, one of the variations that used in this study is Soft Regularized Markov Clustering (SR-MCL). This model is used to ensure that modules on protein interactions do not overlap and can be used for better analysis. The result shows that SR-MCL can be used to determine the cluster from PPI of Sars-Cov-2 and the other related coronavirus.

Original languageEnglish
Article number012012
JournalJournal of Physics: Conference Series
Volume1722
Issue number1
DOIs
Publication statusPublished - 7 Jan 2021
Event10th International Conference and Workshop on High Dimensional Data Analysis, ICW-HDDA 2020 - Sanur-Bali, Indonesia
Duration: 12 Oct 202015 Oct 2020

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