Author

Kelvin Idanwekhai

University of North Carolina at Chapel HillORCID 0000-0002-3804-8830Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Molecular Biology· 1

Signature Assays, Organisms & Methods

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Approach:machine learning (classical)· 2 Approach:Bayesian inference· 1 Organism:human· 1 Assay:protein structure· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Alexander TropshaiD· 1 shared Arianna MinzoniiD· 1 shared Eduardo BarbieriiD· 1 shared Elizabeth BrunkiD· 1 shared Eugene N. MuratoviD· 1 shared Kriti ShuklaiD· 1 shared Martin S. NaradikianiD· 1 shared Michael Angelo-Anthony DanieleiD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperMolecular Biology

Alexander TropshaORCID

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2 papers

Recent·Most cited

Molecular BiologybioRxivPreprintMay 23, 2025

Adaptive Machine Learning Framework enables Unprecedented Yield and Purity of Adeno-Associated Viral Vectors for Gene Therapy

Kelvin Idanwekhai, Shriarjun Shastry, Arianna Minzoni…·Alexander Tropsha

This paper develops a machine learning framework using Bayesian optimization to enhance the yield and purity of AAV vectors in gene therapy.

1 citation

Bayesian inferencemachine learning (classical)

𝕏 PostBluesky

BioinformaticsJournal of Chemical Information and ModelingPeer-reviewedApr 18, 2024

Machine learning of three-dimensional protein structures to predict the functional impacts of genome variation

Kriti Shukla, Kelvin Idanwekhai, Martin S. Naradikian…·Elizabeth Brunk

This paper develops a machine learning method using protein structures to predict the functional impacts of genetic variants on transcriptional pathways of Nrf2 and c-MYC.

4 citations

humanprotein structure

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