Author

Kenneth Doig

The University of MelbourneORCID 0000-0003-0677-9214Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Research Focus:variant effect prediction· 2 Assay:whole-exome sequencing· 2 Research Focus:fine-mapping· 1 Research Focus:GWAS / association mapping· 1 Approach:machine learning (classical)· 1 Research Focus:software / tool· 1 Approach:statistical model· 1

Frequent Co-Authors

ANDREW P. FELLOWESiD· 1 shared David L. GoodeiD· 1 shared Ella R. ThompsoniD· 1 shared Jason LiiD· 1 shared Piers A. BlomberyiD· 1 shared Rashindrie D PereraiD· 1 shared Raveen Rony· 1 shared Richard LupatiD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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1 shared paperGenetic & Genomic Medicine

ANDREW P. FELLOWESORCID

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2 papers

Recent·Most cited

Genetic & Genomic MedicineBreast Cancer ResearchPeer-reviewedJan 13, 2026

Putative breast cancer risk variants from populations of South Asian ancestry are under-represented in public variant classification databases

Raveen Rony, Shenglong Deng, Sarah Yang…·David L. Goode

The paper identifies a significant underrepresentation of South Asian ancestry breast cancer risk variants in ClinVar, highlighting a knowledge gap for this population.

4 citationstop 2%

whole-exome sequencingstatistical model

𝕏 PostBluesky

Genetic & Genomic MedicineBioData MiningPeer-reviewedOct 29, 2025

Using Artificial Intelligence (AI) to Model Clinical Variant Reporting for Next Generation Sequencing (NGS) Oncology Assays

Kenneth Doig, Rashindrie D Perera, Yamuna Kankanige…·Stephen B. Fox

This paper builds machine learning models to predict clinically reportable variants from NGS oncology assays, achieving high precision and recall.

4 citations

whole-exome sequencingmachine learning (classical)

𝕏 PostBluesky