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.
- POLS algorithm
- Protein-protein interaction