S
Saurav Mallik
Researcher at University of Texas Health Science Center at Houston
Publications - 98
Citations - 1295
Saurav Mallik is an academic researcher from University of Texas Health Science Center at Houston. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 16, co-authored 65 publications receiving 736 citations. Previous affiliations of Saurav Mallik include Techno India & Weizmann Institute of Science.
Papers
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A survey and comparative study of statistical tests for identifying differential expression from microarray data
TL;DR: A comprehensive survey on different parametric and non-parametric testing methodologies for identifying differential expression from microarray data sets and their performances have been compared based on some real-life miRNA and mRNA expression data sets.
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The HIV Nef protein modulates cellular and exosomal miRNA profiles in human monocytic cells
Madeeha Aqil,Afsar Raza Naqvi,Saurav Mallik,Sanghamitra Bandyopadhyay,Ujjwal Maulik,Shahid Jameel +5 more
TL;DR: The results demonstrate that Nef causes large-scale dysregulation of cellular miRNAs, including their secretion through exosomes, and suggest this to be a novel viral strategy to affect pathogenesis and to limit the effects of RNA interference on viral replication and persistence.
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An evaluation of supervised methods for identifying differentially methylated regions in Illumina methylation arrays.
TL;DR: A comprehensive evaluation of 4 popular DMR analysis tools under 60 different parameter settings showed that none of the software tools performed best under their default parameter settings, and power varied widely when parameters were changed.
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On the evolution of chaperones and cochaperones and the expansion of proteomes across the Tree of Life
TL;DR: In this article, a systematic analysis depicts that from the simplest archaea to mammals, the total number of proteins per proteome expanded ∼200-fold and individual proteins also became larger, and multidomain proteins expanded ∼50-fold.
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RANWAR: Rank-Based Weighted Association Rule Mining From Gene Expression and Methylation Data
TL;DR: A weighted rule-mining technique to rank the rules using two novel rule-interestingness measures, viz., rank-based weighted condensed support (wcs) and weighted condensed confidence (wcc) measures to bypass the problem of confusion in association rule mining algorithms.