Abstract
Inferring regulatory networks from gene expression data alone is considered a challenging task in systems biology. The introduction of various high-throughput DNA microarray technologies has significantly increased the amount of data to be analysed and various inference algorithms have inherent limitations in dealing with different types of datasets due to their specialized nature. In this paper, we propose a novel method to infer gene regulatory network from expression data which utilises K-means clustering along with some properties of entropy from information theory. The proposed method, first groups the genes of a dataset into a given number of clusters and then finds statistically significant interactions among genes of each individual cluster and selected nearby clusters. To achieve this, an information theoretic approach based on Entropy Reduction is used to generate a regulatory interaction matrix consisting of all genes. The purpose of grouping genes in clusters based on the similarity of expression level is to minimise the search space of regulatory interactions among genes. The performance of the algorithm is measured using precision-recall and compared with the result of ARACNE, a popular information theoretic approach to reverse engineer gene regulatory networks from expression dataset.
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Galib, A.A., Rahman, M.M., Haider Ali, M., Mohammad, E. (2022). Inference of Gene Regulatory Network (GRN) from Gene Expression Data Using K-Means Clustering and Entropy Based Selection of Interactions. In: Islam, A.K.M.M., Uddin, J., Mansoor, N., Rahman, S., Al Masud, S.M.R. (eds) Bangabandhu and Digital Bangladesh. ICBBDB 2021. Communications in Computer and Information Science, vol 1550. Springer, Cham. https://doi.org/10.1007/978-3-031-17181-9_9
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