Identification of certain cancer-mediating genes using Gaussian fuzzy cluster validity index.

In this article, we have used an index, called Gaussian fuzzy index (GFI), recently developed by the authors, based on the notion of fuzzy set theory, for validating the clusters obtained by a clustering algorithm applied on cancer gene expression data. GFI is then used for the identification of genes that have altered quite significantly from normal state to carcinogenic state with respect to their mRNA expression patterns. The effectiveness of the methodology has been demonstrated on three gene expression cancer datasets dealing with human lung, colon and leukemia. The performance of GFI is compared with 19 exiting cluster validity indices. The results are appropriately validated biologically and statistically. In this context, we have used biochemical pathways, p-value statistics of GO attributes, t-test and zscore for the validation of the results. It has been reported that GFI is capable of identifying high-quality enriched clusters of genes, and thereby is able to select more cancer-mediating genes.

Modularized study of human calcium signalling pathway.

Signalling pathways are complex biochemical networks responsible for regulation of numerous cellular functions. These networks function by serial and successive interactions among a large number of vital biomolecules and chemical compounds. For deciphering and analysing the underlying mechanism of such networks,a modularized study is quite helpful. Here we propose an algorithm for modularization of calcium signalling pathway of H. sapiens .The idea that "a node whose function is dependent on maximum number of other nodes tends to be the center of a sub network" is used to divide a large signalling network into smaller sub networks. Inclusion of node(s) into sub networks(s) is dependent on the outdegree of the node(s). Here outdegree of a node refers to the number of relations of the considered node lying outside the constructed sub network. Node(s) having more than c relations lying outside the expanding sub network have to be excluded from it. Here c is a specified variable based on user preference, which is finally fixed during adjustments of created sub networks, so that certain biological significance can be conferred on them.