TY - JOUR
T1 - Application of bimax, pols, and lcm-mbc to find bicluster on interactions protein between hiv-1 and human
AU - Swasti, Olivia
AU - Kaloka, Tesdiq P.
AU - Siswantining, Titin
AU - Bustamam, Alhadi
N1 - Publisher Copyright:
© 2020, Austrian Statistical Society. All rights reserved.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Biclustering, in general, is a process of clustering genes and conditions simultaneously rather than clustering them separately. The purpose of biclustering is to discover a subset from experimental data. Further, biclustering results can be analyzed from a biological perspective. Biclustering can also be used for protein-protein interaction. In protein-protein interaction, biclustering can cluster interactions based on rows and columns. In this research, we applied three biclustering algorithms based on graph approach, Binary inclusion-Maximal (BiMax), local search framework based on pairs operation (POLS), and (LCM-MBC) to clustering data of protein-protein interaction between HIV-1 and human. We change the interaction protein-protein interaction data into binary then divided into two datasets called HV positive and HV negative. Then compare the biclustering results of each dataset using heatmap and analyze them with GO terms. From dataset HV positive, BiMax found 30 biclusters, LCM-MBC 31 biclusters, and POLS 13 biclusters. From dataset HV negative, BiMax found eight biclusters, LCM-MBC 14 bicluster, and POLS 10 biclusters. Based on the results of the heatmap, all bicluster entry from BiMax is a protein that interacts, whereas biclusters entry of LCM-MBC and POLS still have proteins that do not interact. It can be concluded that BiMax algorithm is good for clustering protein-protein interaction, especially for binary data.
AB - Biclustering, in general, is a process of clustering genes and conditions simultaneously rather than clustering them separately. The purpose of biclustering is to discover a subset from experimental data. Further, biclustering results can be analyzed from a biological perspective. Biclustering can also be used for protein-protein interaction. In protein-protein interaction, biclustering can cluster interactions based on rows and columns. In this research, we applied three biclustering algorithms based on graph approach, Binary inclusion-Maximal (BiMax), local search framework based on pairs operation (POLS), and (LCM-MBC) to clustering data of protein-protein interaction between HIV-1 and human. We change the interaction protein-protein interaction data into binary then divided into two datasets called HV positive and HV negative. Then compare the biclustering results of each dataset using heatmap and analyze them with GO terms. From dataset HV positive, BiMax found 30 biclusters, LCM-MBC 31 biclusters, and POLS 13 biclusters. From dataset HV negative, BiMax found eight biclusters, LCM-MBC 14 bicluster, and POLS 10 biclusters. Based on the results of the heatmap, all bicluster entry from BiMax is a protein that interacts, whereas biclusters entry of LCM-MBC and POLS still have proteins that do not interact. It can be concluded that BiMax algorithm is good for clustering protein-protein interaction, especially for binary data.
KW - Biclustering
KW - BiMax
KW - Graph
KW - LCM-MBC
KW - POLS algorithm
KW - Protein-protein interaction
UR - http://www.scopus.com/inward/record.url?scp=85083824605&partnerID=8YFLogxK
U2 - 10.17713/ajs.v49i3.1011
DO - 10.17713/ajs.v49i3.1011
M3 - Article
AN - SCOPUS:85083824605
SN - 1026-597X
VL - 49
SP - 1
EP - 18
JO - Austrian Journal of Statistics
JF - Austrian Journal of Statistics
IS - 3 Special Issue
ER -