TY - JOUR
T1 - Application of soft regularized markov clustering for analyzing protein-protein interaction in sars-cov-2 and other related coronavirus
AU - Pratiwi, S. A.
AU - Bustamam, A.
AU - Sarwinda, D.
N1 - Funding Information:
This work has been supported in part by PUTI 2020 research grant from University of Indonesia with contract number NKB-778/UN2.RST/HKP.05.00/2020.
Publisher Copyright:
© 2021 Institute of Physics Publishing. All rights reserved.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/1/7
Y1 - 2021/1/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85100825588&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1722/1/012012
DO - 10.1088/1742-6596/1722/1/012012
M3 - Conference article
AN - SCOPUS:85100825588
SN - 1742-6588
VL - 1722
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012012
T2 - 10th International Conference and Workshop on High Dimensional Data Analysis, ICW-HDDA 2020
Y2 - 12 October 2020 through 15 October 2020
ER -
