YEAST GENE EXPRESSION ANALYSIS USING K MEANS AND FCM

Abstract

The experiments on gene expression analysis  is to analyze  the thousands of genes at once to know the global picture of cell function which aids to study the regulatory gene defects like cancer and other devastating diseases, cellular responses to the environment and cell cycle variation etc. The determination of the pattern of genes help to identify the  level of genetic transcription under various time periods. In  this work, we apply partitional clustering algorithms such as K Means and Fuzzy C Means clustering on yeast gene expression profiles to group the similar genes. The validity of clusters is analyzed with the Davis Bouldin    Index(DBI). FCM has achieved the DBI of 0.31452 for K=3 and 0.37822 for K=4 which is better than K Means clustering.