Finding correlated biclusters from microarray data using the modified lift algorithm based on new residue score Online publication date: Thu, 18-Mar-2021
by Alhadi Bustamam; Soeganda Formalidin; Titin Siswantining; Zuherman Rustam
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 24, No. 4, 2020
Abstract: The purpose of this research is to find a strong correlation between genes and conditions of diabetes mellitus gene expression data from obese and lean people using three-phase biclustering. The first step is to use Singular Value Decomposition (SVD) to decompose matrix gene expression data into two global-based gene and condition matrices. The second step is to use Partition around Medoid (PAM) to cluster gene and condition-based matrices using Euclidean distance, forming several biclusters that were further evaluated using the Modified Lift Algorithm based on Pearson correlation, which is a very appropriate method to detect an additive-multiplicative bicluster type. The algorithm processes are run using open-source R software. The resulting biclusters of the proposed algorithm having a strong correlation among genes and samples are obtained so that the method has high potential in future medical research.
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