Author

Deepti R. Nagarkar

Genmab (United States)ORCID 0000-0002-9928-6787Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 2 Assay:clinical cohort / trial· 1 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Approach:large language model· 1 Approach:statistical model· 1 Research Focus:variant calling· 1 Assay:whole-exome sequencing· 1

Frequent Co-Authors

Arshad AhmadiD· 1 shared Brandon W. HiggsiD· 1 shared Christian HammeriD· 1 shared Diana ChangiD· 1 shared Eda Zhou· 1 shared Edward Ernest KadeliD· 1 shared Han SiiD· 1 shared Jason A. Vander HeideniD· 1 shared

Activity by Year

2020-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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1 shared paperCancer Biology

Arshad AhmadORCID

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2 papers

Recent·Most cited

Cancer BiologyCurrent OncologyPeer-reviewedSep 30, 2025

Mechanistically explainable AI model for predicting synergistic cancer therapy combinations

Han Si, Sanyam Kumar, Sneh Lata…·Brandon W. Higgs

This paper introduces an LLM-based framework that predicts synergistic cancer drug combinations using drug data and a knowledge graph, achieving an F1 score of 0.80.

3 citations

humanclinical cohort / trial

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BioinformaticsBriefings in BioinformaticsPeer-reviewedAug 20, 2020

In silico tools for accurate HLA and KIR inference from clinical sequencing data empower immunogenetics on individual-patient and population scales

Jieming Chen, Shravan Madireddi, Deepti R. Nagarkar…·Christian Hammer

The paper evaluates computational tools for accurately inferring HLA alleles and KIR haplotypes from whole-genome or whole-exome sequencing data, showing high accuracy for both tasks.

31 citations

humanwhole-genome sequencing

𝕏 PostBluesky