A Novel Soft Clustering Approach for Gene Expression Data  

作　　者：E.Kavitha R.Tamilarasan Arunadevi Baladhandapani M.K.Jayanthi Kannan 

机构地区：[1]A Constituent College of Anna University,University College of Engineering,Villupuram,605103,India [2]A Constituent College of Anna University,University College of Engineering,Pattukkottai,614701,India [3]Department of Electronics and Communication Engineering,Dr.N.G.P Institute of Technology,Coimbatore,641048,India [4]Department of Computer Science Engineering,Faculty of Engineering and Technology,JAIN(Deemed to be University),Bangalore,562112,India

出　　处：《Computer Systems Science & Engineering》2022年第12期871-886,共16页计算机系统科学与工程（英文）

摘　　要：Gene expression data represents a condition matrix where each rowrepresents the gene and the column shows the condition. Micro array used todetect gene expression in lab for thousands of gene at a time. Genes encode proteins which in turn will dictate the cell function. The production of messengerRNA along with processing the same are the two main stages involved in the process of gene expression. The biological networks complexity added with thevolume of data containing imprecision and outliers increases the challenges indealing with them. Clustering methods are hence essential to identify the patternspresent in massive gene data. Many techniques involve hierarchical, partitioning,grid based, density based, model based and soft clustering approaches for dealingwith the gene expression data. Understanding the gene regulation and other usefulinformation from this data can be possible only through effective clustering algorithms. Though many methods are discussed in the literature, we concentrate onproviding a soft clustering approach for analyzing the gene expression data. Thepopulation elements are grouped based on the fuzziness principle and a degree ofmembership is assigned to all the elements. An improved Fuzzy clustering byLocal Approximation of Memberships (FLAME) is proposed in this workwhich overcomes the limitations of the other approaches while dealing with thenon-linear relationships and provide better segregation of biological functions.

关 键 词：REINFORCEMENT MEMBERSHIP CENTROID threshold STATISTICS BIOINFORMATICS gene expression data 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]