Author

Yatish Jain

Commonwealth Scientific and Industrial Research OrganisationORCID 0000-0002-0556-6586Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Genetic & Genomic Medicine· 2 Cancer Biology· 1 Molecular Biology· 1

Signature Assays, Organisms & Methods

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Organism:human· 4 Approach:machine learning (classical)· 4 Research Focus:benchmarking· 3 Assay:GWAS summary statistics· 2 Approach:statistical model· 2 Research Focus:variant effect prediction· 2 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1

Frequent Co-Authors

Denis C. BaueriD· 6 shared Brendan HoskingiD· 5 shared Natalie A. TwineiD· 4 shared Roc ReguantiD· 3 shared Anne H. KleiniD· 2 shared Anubhav KaphleiD· 2 shared Arash BayatiD· 2 shared Daniel RetiiD· 2 shared

Activity by Year

2019-2025 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 35 mutual co-author links

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6 shared papersGenetic & Genomic Medicine

Denis C. BauerORCID

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6 papers

Recent·Most cited

Machine Learning-Driven Discovery of Synergistic Protein Interactions Identifies ATL3 as a Putative Biomarker for Cancer Drug Response

Priya Ramarao-Milne, Anubhav Kaphle, Roc Reguant, Anne H. Klein, Michael J. Kuiper, Brendan Hosking, Julika Wenzel, Hawlader Abdullah Al-Mamun, Lujain Elazab, Qing Zhong, Roger R. Reddel, Laurence Wilson, Yatish Jain, Natalie A. Twine, Denis C. Bauer

The paper introduces synerOmics, a machine learning framework to identify synergistic protein interactions, finding ATL3 as a biomarker for lapatinib resistance in breast cancer.

bioRxiv · Nov 5, 2025 · PreprintCancer Biology

Novel Epistatic Interaction Between RBMS3 and CDKN2B-AS1 in Coronary Artery Disease Risk Identified by Machine Learning Tool VariantSpark

Letitia M.F. Sng, Mitchell J. O’Brien, Brendan Hosking, Piotr Szul, Roc Reguant, Mythreye Venkatesan, Philip J. Freda, Zhiping Paul Wang, Jason H. Moore, Anne H. Klein, Michael J. Kuiper, Angus Panagopoulos, Johan W. H. Verjans, Yatish Jain, Denis C. Bauer, Natalie A. Twine

The paper identifies an epistatic interaction between RBMS3 and CDKN2B-AS1 in CAD risk using VariantSpark and BitEpi, with validation in UK Biobank and All of Us.

medRxiv · Oct 19, 2025 · PreprintGenetic & Genomic Medicine

Optimizing UK Biobank Cloud Based Research Analysis Platform to Fine Map Coronary Artery Disease Loci in Whole Genome Sequencing Data

Letitia M.F. Sng, Anubhav Kaphle, Mitchell J. O’Brien, Brendan Hosking, Roc Reguant, Johan W. H. Verjans, Yatish Jain, Natalie A. Twine, Denis C. Bauer

The paper fine maps CAD loci in UK Biobank whole genome sequencing data using PolyFun and ML tools, showing improved sensitivity over traditional methods and demonstrating cost and speed benefits of cloud-based analysis.

Scientific Reports · Mar 25, 2025Peer-reviewedGenetic & Genomic Medicine

Optimized nickase- and nuclease-based prime editing in human and mouse cells

Fatwa Adikusuma, Caleb Lushington, Jayshen Arudkumar, G.I. Godahewa, Yu Chinn Joshua Chey, Luke Gierus, Sandra Piltz, Ashleigh Geiger, Yatish Jain, Daniel Reti, Laurence O W Wilson, Denis C. Bauer, Paul Quinton Thomas

This paper develops PEA1 plasmids and PE-nuclease systems for efficient prime editing in human and mouse cells, achieving high editing efficiency in some cell lines and fetal mice.

Nucleic Acids Research · Sep 16, 2021Peer-reviewedMolecular Biology

BitEpi: A Fast and Accurate Exhaustive Higher-Order Epistasis Search

Arash Bayat, Brendan Hosking, Yatish Jain, Cameron Hosking, Milindi Kodikara, Daniel Reti, Natalie A. Twine, Denis C. Bauer

BitEpi efficiently identifies higher-order epistatic interactions among SNVs in genetic data, outperforming existing methods in speed and accuracy.

Scientific Reports · Aug 5, 2021Peer-reviewedBioinformatics

VariantSpark, A Random Forest Machine Learning Implementation for Ultra High Dimensional Data

Arash Bayat, Piotr Szul, Aidan R. O’Brien, Robert A. Dunne, Oscar Junhong Luo, Yatish Jain, Brendan Hosking, Denis C. Bauer

This paper introduces CursedForest, a Random Forest implementation that scales to millions of features and outperforms existing methods in speed.

bioRxiv · Jul 15, 2019 · PreprintBioinformatics