CLUSTERING MICROARRAY GENE EXPRESSION DATA USING FUZZY C-MEANS AND DTW DISTANCE
Clustering analysis of data from DNA microarray hybridization studies is essential for identifying biologically relevant groups of genes.Partitional clustering methods such as K-means or self-organizing maps assign each gene to a single cluster.However, these methods do not provide information about the influence of a given gene for the overall shape of clusters.Here we apply a fuzzy partitioning method, Fuzzy C-means (FCM), to attribute cluster membership values to genes.Gene expressions are expected to vary not only in terms of expression amplitudes, but also in terms of time progression since biological processes may unfold with different rates in response to different experimental conditions or within different organisms and individuals.Any distance (Euclidean, Manhattan,...) which aligns the ith point on one expression with the ith point on the other will produce a poor similarity score.In this paper we use DTW distance to attain expressions similarity.
Microarray FCM DTW
H.TAGHIZAD A.MEHRIDEHNAVI
Dept. of Medical Physics and Biomedical Engineering,and MISP Center. Isfahan University of Medical Sciences,Iran
国际会议
成都
英文
395-399
2011-11-25（万方平台首次上网日期，不代表论文的发表时间）