Biclustering using Parallel Fuzzy Approach for Analysis of Microarray Gene Expression Data

January 30, 2018 | Author: AI Coordinator - CSC Journals | Category: Message Passing Interface, Cluster Analysis, Parallel Computing, Applied Mathematics, Areas Of Computer Science
Share Embed


Short Description

Description: Biclusters are required to analyzing gene expression patterns of genes comparing rows in expression profile...

Description

Biclusters are required to analyzing gene expression patterns of genes comparing rows in expression profiles and analyzing expression profiles of samples by comparing columns in gene expression matrix. In the process of biclustering we need to cluster genes and samples. The algorithm presented in this paper is based upon the two-way clustering approach in which the genes and samples are clustered using parallel fuzzy C-means clustering using message passing interface, we call it MFCM. MFCM applied for clustering on genes and samples which maximize membership function values of the data set. It is a parallelized rework of a parallel fuzzy two-way clustering algorithm for microarray gene expression data [9], to study the efficiency and parallelization improvement of the algorithm. The algorithm uses gene entropy measure to filter the clustered data to find biclusters. The method is able to get highly correlated biclusters of the gene expression dataset.
View more...

Comments

Copyright © 2017 DOCLEGEND.COM Inc.