Author

Sanguthevar Rajasekaran

University of ConnecticutORCID 0000-0002-0137-4843Unclaimed ORCID profile · follow to get notified of new preprints

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Sanguthevar Rajasekaran1 paper

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:gene regulatory network inference· 1 Approach:graph neural network· 1

Frequent Co-Authors

Subrata SahaiD· 3 shared Ahmed T. SolimaniD· 2 shared Zigeng WangiD· 1 shared

Activity by Year

2020-2021 · peak 2/yr

Co-Author Collaboration Network

3 top collaborators · 2 mutual co-author links

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3 shared papersBioinformatics

Subrata SahaORCID

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3 papers

Recent·Most cited

BioinformaticsbioRxivPreprintOct 19, 2021

Novel graph theoretic biological pathway network analytics methods for analyzing and discovering Alzheimer's disease related genes

Subrata Saha, Ahmed T. Soliman, Sanguthevar Rajasekaran

The paper introduces a graph-theoretic method to analyze Alzheimer's disease-related genes and identifies three functional modules and 24 novel AD-related genes.

DOI 10.1101/2021.10.19.465019

humangraph neural network

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BioinformaticsIEEE International Conference on Bioinformatics and Biomedicine (BIBM)Peer-reviewedDec 16, 2020

RSGSA: a Robust and Stable Gene Selection Algorithm

Subrata Saha, Ahmed T. Soliman, Sanguthevar Rajasekaran

RSGSA is a supervised gene selection algorithm that improves stability and classification accuracy in gene expression datasets with phenotypes.

DOI 10.1109/bibm49941.2020.9313187

bulk RNA-seqGWAS summary statistics

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BioinformaticsbioRxivPreprintOct 1, 2020

A novel algorithm to accurately classify metagenomic sequences

Subrata Saha, Zigeng Wang, Sanguthevar Rajasekaran

This paper introduces a novel algorithm, HMSC, for accurately classifying metagenomic sequences and determining microbial abundances, showing improved accuracy and efficiency over existing methods.

DOI 10.1101/2020.10.01.321067

metagenomicsstatistical model

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