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Sushmita Paul

Researcher at Indian Institute of Technology, Jodhpur

Publications -  62
Citations -  712

Sushmita Paul is an academic researcher from Indian Institute of Technology, Jodhpur. The author has contributed to research in topics: Cluster analysis & Rough set. The author has an hindex of 12, co-authored 57 publications receiving 575 citations. Previous affiliations of Sushmita Paul include University of Erlangen-Nuremberg & Indian Statistical Institute.

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Journal ArticleDOI

Gene expression and protein---protein interaction data for identification of colon cancer related genes using f-information measures

TL;DR: Results indicate that the integrated method presented is quite promising and may become a useful tool for identifying disease genes.
Book

Scalable Pattern Recognition Algorithms: Applications in Computational Biology and Bioinformatics

TL;DR: This book addresses the need for a unified framework describing how soft computing and machine learning techniques can be judiciously formulated and used in building efficient pattern recognition models.
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Genome-wide Analysis of Multi-View Data of miRNA-seq to Identify miRNA Biomarkers for Stomach Cancer

TL;DR: A computational framework is proposed that selects different sets of miRNAs for five different categories of clinical outcomes viz. condition, clinical stage, age, histological type, and survival status and has been validated quantitatively and through biological significance analysis.
Journal ArticleDOI

Mycobacterial Cord Factor Reprograms the Macrophage Response to IFN-γ towards Enhanced Inflammation yet Impaired Antigen Presentation and Expression of GBP1.

TL;DR: Investigation of the cross-regulation of the mouse macrophage transcriptome by IFN-γ and by TDM or its synthetic analogue trehalose-6,6-dibehenate revealed a significant degree of negative regulation of IFN–γ–induced Ag presentation and antimicrobial gene expression by the mycobacterial cord factor that may contribute to myc Cobacterial persistence.
Journal ArticleDOI

Identification of miRNA-mRNA Modules in Colorectal Cancer Using Rough Hypercuboid Based Supervised Clustering.

TL;DR: This study presents an application of the RH-SAC algorithm on miRNA and mRNA expression data for identification of potential miRNA-mRNA modules and identified novel miRNA/mRNA interactions in colorectal cancer.