Premium
Comparative Analysis of Cluster Validity Indices in Identifying Some Possible Genes Mediating Certain Cancers
Author(s) -
Ghosh Anupam,
Dhara Bibhas Chandra,
De Rajat K.
Publication year - 2013
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201200142
Subject(s) - cluster (spacecraft) , cluster analysis , identification (biology) , computational biology , gene cluster , gene , value (mathematics) , data mining , selection (genetic algorithm) , cancer , biology , computer science , bioinformatics , genetics , artificial intelligence , machine learning , programming language , botany
Abstract In this article, we compare the performance of 19 cluster validity indices, in identifying some possible genes mediating certain cancers, based on gene expression data. For the purpose of this comparison, we have developed a method. The proposed method involves cluster generation, selection of the best k ‐value or c ‐values, cluster identification, identifying the altered gene cluster, scoring an altered gene cluster and determining the best k ‐value or c ‐value exploring through biological repositories. The effectiveness of the method has been demonstrated on three gene expression data sets dealing with human lung cancer, colon cancer, and leukemia. Here, we have used three clustering algorithms, i.e., k ‐means, PAM and fuzzy c ‐means. We have used biochemical pathways related to these cancers and p ‐value statistics for validating the study.