Showing papers by "Sushmita Paul published in 2012"

Rough-fuzzy c-means for clustering microarray gene expression data

Abstract: Clustering technique is one of the useful tools to elucidate similar patterns across large number of transcripts and to identify likely co-regulated genes. It attempts to partition the genes into groups exhibiting similar patterns of variation in expression level. An application of rough-fuzzy c-means (RFCM) algorithm is presented in this paper to discover co-expressed gene clusters. Selection of initial prototypes of different clusters is one of the major issues of the RFCM based microarray data clustering. The pearson correlation based initialization method is used to address this limitation. It enables the RFCM algorithm to discover co-expressed gene clusters. The effectiveness of the RFCM algorithm and the initialization method, along with a comparison with other related methods, is demonstrated on five yeast gene expression data sets using standard cluster validity indices and gene ontology based analysis.

Robust RFCM algorithm for identification of co-expressed miRNAs

Abstract: MicroRNAs (miRNAs) are short, endogenous RNAs having ability to regulate gene expression at the post-transcriptional level. Various studies have revealed that miRNAs tend to cluster on chromosomes. Members of a cluster that are at close proximity on chromosome are highly likely to be processed as cotranscribed units. Therefore, a large proportion of miRNAs are co-expressed. Expression profiling of miRNAs generates a huge volume of data. Complicated networks of miRNA-mRNA interaction create a big challenge for scientists to decipher this huge expression data. In order to extract meaningful information from expression data, this paper presents the application of robust rough-fuzzy c-means (rRFCM) algorithm to discover co-expressed miRNA clusters. The rRFCM algorithm comprises a judicious integration of rough sets, fuzzy sets, and c-means algorithm. The effectiveness of the rRFCM algorithm and different initialization methods, along with a comparison with other related methods, is demonstrated on three miRNA microarray expression data sets using Silhouette index, Davies-Bouldin index, Dunn index, β index, and gene ontology based analysis.

Rough sets and support vector machine for selecting differentially expressed miRNAs

Abstract: The microRNAs, also known as miRNAs are, the class of small non-coding RNAs that repress the expression of a gene post-transcriptionally. In effect, they regulate expression of a gene or protein. It has been observed that they play an important role in various cellular processes and thus help in carrying out normal functioning of a cell. However, dysregulation of miRNAs is found to be a major cause of a disease. Various studies have also shown the role of miRNAs in cancer and utility of miRNAs for the diagnosis of cancer and other diseases. A large number of works have been conducted to identify differentially expressed miRNAs as unlike with mRNA expression, a modest number of miRNAs might be sufficient to classify human cancers. In this regard, this paper presents a rough set based feature selection algorithm to select miRNAs from expression data that can classify tissue samples into their respective category with minimal error rate. It selects a set of miRNAs by maximizing both the relevance and significance of miRNAs. The effectiveness of the rough set based algorithm, along with a comparison with other related algorithms, is demonstrated on three miRNA microarray expression data sets using the B.632+ bootstrap error rate of support vector machine.