Author

Malvika Sudhakar

Indian Institute of Technology MadrasORCID 0000-0002-2442-4305Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Organism:human· 3 Assay:bulk RNA-seq· 2 Approach:machine learning (classical)· 2 Assay:whole-exome sequencing· 2 Research Focus:benchmarking· 1 Research Focus:differential expression· 1 Research Focus:gene regulatory network inference· 1 Assay:multiome· 1

Frequent Co-Authors

Karthik RamaniD· 3 shared Raghunathan RengaswamyiD· 2 shared Aakansha RaiiD· 1 shared Ádám Misák· 1 shared Akash Gosain· 1 shared Alexander Wu· 1 shared Alice DriesseniD· 1 shared Alidivinas Prusokas· 1 shared

Activity by Year

2021-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersBioinformatics

Karthik RamanORCID

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3 papers

Recent·Most cited

BioinformaticsNAR Genomics and BioinformaticsPeer-reviewedJul 9, 2022

iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data

Anjana Anilkumar Sithara, Devi Priyanka Maripuri, Keerthika Moorthy…·Karthik Raman

iCOMIC is a user-friendly bioinformatics pipeline for analyzing cancer omics data, providing insightful statistics and high accuracy in variant calling.

3 citations

humanwhole-genome sequencing

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BioinformaticsFrontiers in GeneticsPeer-reviewedMay 10, 2022

Multi-omic data helps improve prediction of personalised tumor suppressors and oncogenes

Malvika Sudhakar, Raghunathan Rengaswamy, Karthik Raman

This study introduces PIVOT, a multi-omic machine learning model that classifies genes as tumor suppressors, oncogenes, or neutral, improving prediction accuracy for BRCA, LUAD, and COAD datasets.

8 citations

humanwhole-exome sequencing

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BioinformaticsMolecular Systems BiologyPeer-reviewedOct 1, 2021

Cell-to-cell and type-to-type heterogeneity of signaling networks: Insights from the crowd

Attila Gábor, Marco Tognetti, Alice Driessen…·Mingyu Lee

The paper organizes a DREAM challenge to benchmark predictive models of single-cell signaling using mass cytometry data from breast cancer cell lines.

17 citations

humanmultiome

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