Showing papers by "Sushmita Paul published in 2018"

Robust Computational Method for Identification of miRNA-mRNA Modules in Cervical Cancer

Abstract: Cervical cancer is a leading severe malignancy throughout the world. Molecular processes and biomarkers leading to tumor progression in cervical cancer are either unknown or only partially understood. An increasing number of studies have shown that microRNAs play an important role in tumorigenesis so understanding the regulatory mechanism of miRNAs in gene-regulatory network will help elucidate the complex biological processes that occur during malignancy. Identification of microRNA-messengerRNA (miRNA-mRNA) regulatory modules will aid deciphering aberrant transcriptional regulatory network in cervical cancer but is computationally challenging. In this regard, an algorithm, termed as relevant and functionally consistent miRNA-mRNA modules (RFCM3), is proposed. It integrates miRNA and mRNA expression data of cervical cancer for identification of potential miRNA-mRNA modules. It selects a miRNA-mRNA module by maximizing relation of mRNAs with miRNA and functional similarity between selected mRNAs. Later using the knowledge of miRNA-miRNA synergistic network different modules are fused and finally a set of modules are generated containing several miRNAs as well as mRNAs. This type of module explains the underlying biological pathways containing multiple miRNAs and mRNAs. The effectiveness of the proposed approach over other existing methods has been demonstrated on a miRNA and mRNA expression data of cervical cancer with respect to enrichment analyses and other standard metrices. The proposed approach was found to generate more robust, integrated, and functionally enriched miRNA-mRNA modules in cervical cancer.

Type 2 Diabetes Gene Identification Using an Integrated Approach from Single-Cell RNA Sequencing Data

Abstract: Increase in number of people diagnosed with diabetes makes this disease a new health threat in the 21st century. Understanding the etiology of and finding a way to prevent diabetes, especially type 2 diabetes mellitus, is an urgent challenge for the health care community and our society. Pancreatic islet cells are responsible for maintaining normal blood glucose level and if there is any disturbance that leads to the onset of diabetes. Human pancreatic islet cells contain $\alpha$,$\beta$,$\delta$, and PP cells. Understanding the contribution of each type of cell through gene expression in type 2 diabetes mellitus is very important for the development of diagnostic tools. Therefore, gene expression data of $\alpha$,$\beta$,$\delta$ and PP cells can be used. Single cell RNA sequencing technology has been found useful to generate expression data for individual cells. The gene expression data is usually used to find genes that are related to clinical outcome. However, in a biological process a set of genes are involved that share functional similarity. Analysing only single type of data may not generate significant type 2 diabetes mellitus genes. In this regard, an integrated approach has been used to analyse single-cell RNA sequencing data of human pancreatic islet cells. The integrated approach is designed by incorporating protein-protein interaction network data and gene expression data to select a set of genes that are highly related to diabetes also they are functionally related among themselves. The effectiveness of the approach is demonstrated over other existing methods.