Application of BiMax, POLS, and LCM-MBC to Find Bicluster on Interactions Protein between HIV-1 and Human

Abstract

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.

Efficient Exact Algorithms for Maximum Balanced Biclique Search in Bipartite Graphs

“…Please ignore the colour differences in this example. Clearly, all maximal bicliques involving 2 and 3 are contained in Figures 3(a For instance, given the bidegeneracy order of the graph in Figure 1(a), BDorder= (1,7,2,6,11,12,8,3,4,5,9,10), vertices 3 and 4 centred subgraphs are black and red parts of Figures 3(a) and (b) respectively. A search order for tightening search space.…”

“…Furthermore, it plays principal roles for analyzing biological data since an MBB is an important instance of bicluster [7,28]. Recently, it also draws significant attention for discovering interactions between proteins [4,19,12,14].…”

Performance of multivariate mutual information and autocorrelation encoding methods for the prediction of protein-protein interactions

“…Until now, various types of coding techniques and machine learning-based computing methods for determining whether or not proteins interact have been widely developed. According to the previous research there are some methods that using information of evolution [6], using natural language processing [7], and using clustering methods [8], [9] to learn protein-protein interaction. The most popular method is predicting the interaction between two proteins based on the sequences of amino acids.…”

Analisis Hasil Bicluster Algoritma Pols Pada Interaksi Protein Manusia Dan Hiv-1

“…HIV-1 dapat menyerang kekebalan tubuh manusia karena terjadi proses interaksi protein. Sifat dan fungsi suatu protein dapat diketahui dengan mengelompokkan interaksi protein [3]. Sifat dan fungsi protein HIV-1 yang telah diketahui akan memudahkan pihak kesehatan untuk dapat menemukan cara penyembuhan penderita penyakit AIDS.…”

“…Algoritma POLS menggunakan pendekatan teori graf dan teori biclique seimbang. Berdasarkan penelitian Bustamam [3], hasil bicluster Algoritma POLS menunjukkan adanya protein yang tidak berinteraksi pada satu bicluster. Kemampuan suatu Algortima Bicluster dalam mengelompokkan data diukur dengan tingkat kepadatannya, semakin tinggi tingkat kepadatannya, maka semakin baik algoritma tersebut.…”