Author

Kate F. Kernan

University of PittsburghORCID 0000-0002-6337-841XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Genetic & Genomic Medicine· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:benchmarking· 1 Research Focus:deconvolution· 1 Research Focus:differential expression· 1 Assay:GWAS summary statistics· 1 Organism:human· 1 Approach:statistical model· 1

Frequent Co-Authors

Hyun Jung ParkiD· 2 shared Joseph A. CarcilloiD· 2 shared Soyeon KimiD· 2 shared Aditya SriramiD· 1 shared Panayiotis V. BenosiD· 1 shared Rebecca Bohn· 1 shared Scott CannaiD· 1 shared Zhenjiang FaniD· 1 shared

Activity by Year

2021-2024 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 22 mutual co-author links

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2 shared papersGenetic & Genomic Medicine

Hyun Jung ParkORCID

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2 papers

Recent·Most cited

Genetic & Genomic MedicinemedRxivPreprintOct 15, 2024

DeepEXPOKE: A Deep Learning Framework with Polygenic Risk Scores as Knockoffs for Deconvoluting Genetic and Non-Genetic Exposure Risks in Sepsis and Coronary Heart Disease

Aditya Sriram, Rebecca Bohn, Kate F. Kernan…·Hyun Jung Park

DeepEXPOKE uses deep learning with knockoff features to distinguish genetic and non-genetic exposure risks in sepsis and coronary heart disease, identifying metabolites and asthma as risk factors.

1 citation

humanGWAS summary statistics

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BioinformaticsbioRxivPreprintJul 17, 2021

Causal inference using deep-learning variable selection identifies and incorporates direct and indirect causalities in complex biological systems

Zhenjiang Fan, Kate F. Kernan, Panayiotis V. Benos…·Hyun Jung Park

This paper introduces causalDeepVASE, a deep-learning method for causal inference that identifies both direct and indirect causal relationships in biological systems, outperforming existing methods in simulated and biological data.

1 citation

deep learning

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