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Clustering gene Expression Data Through Modified Agglomerative M-CURE Hierarchical Algorithm

Computer Systems Science & Engineering 2022年第6期41卷 1027-1041页

作者： E.Kavitha R.Tamilarasan N.Poonguzhali M.K.Jayanthi Kannan A Constituent College of Anna University University College of EngineeringVillupuram605103India A Constituent College of Anna University University College of EngineeringPattukkottai614701India Department of Computer Science and Engineering Manakula Vinayagar Institute of TechnologyPuducherry605107India Department of Computer Science Engineering Faculty of Engineering and TechnologyJAIN(Deemed-To-Be University)Bangalore562112India

gene expression refers to the process in which the gene information isused in the functional gene product synthesis. They basically encode the proteinswhich in turn dictate the functionality of the cell. The first step in gene expressionstudy involves the clustering usage. This is due to the reason that biological networks are very complex and the genes volume increases the comprehending challenges along with the data interpretation which itself inhibit vagueness, noise andimprecision. For a biological system to function, the essential cellular moleculesmust interact with its surrounding including RNA, DNA, metabolites and proteins. Clustering methods will help to expose the structures and the patterns inthe original data for taking further decisions. The traditional clustering techniquesinvolve hierarchical, model based, partitioning, density based, grid based and softclustering methods. Though many of these methods provide a reliable output inclustering, they fail to incorporate huge data of gene expressions. Also, thereare statistical issues along with choosing the right method and the choice of dissimilarity matrix when dealing with gene expression data. We propose to use amodified clustering algorithm using representatives (M-CURE) in this workwhich is more robust to outliers as compared to K-means clustering and also ableto find clusters with size variances.

关键词： Clustering gene identifiers representatives dimension reduction

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