Author

Joshua D. Smith

University of PittsburghORCID 0000-0002-2076-7589Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Joshua D. Smith1 paper

Biology Disciplines

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Cancer Biology· 1 Genetic & Genomic Medicine· 1

Signature Assays, Organisms & Methods

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Organism:human· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Assay:electronic health records· 1

Frequent Co-Authors

Adrian V. LeeiD· 1 shared Ali G. GharaviiD· 1 shared Alicia Yiran ZhouiD· 1 shared Amy TayloriD· 1 shared Andreas A. Theodorou· 1 shared Andrew HaddadiD· 1 shared Anjene Musick· 1 shared Anthony PhilippakisiD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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1 shared paperCancer Biology

Adrian V. LeeORCID

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2 papers

Recent·Most cited

Integrating Artificial Intelligence-Driven Digital Pathology and Genomics to Establish Patient-Derived Organoids as a Novel Alternative Model for Drug Response in Head and Neck Cancer

Rose Doerfler, Jie Chen, Carl Kim, Joshua D. Smith, Micah K. Harris, Krishna Beer Singh, Brian Robert Isett, Rebekah Dadey, Daniel D. Brown, Adrian V. Lee, Xuefeng Wang, Matthew E. Spector, Seungwon Kim, Shaum Sridharan, Kevin J. Contrera, , Riyue Bao

This study establishes patient-derived organoids (PDOs) for HNSCC drug response testing, integrating AI-driven histopathology and multi-omics analysis to create a scalable preclinical model.

bioRxiv · Jun 24, 2025 · PreprintCancer Biology

The frequency of pathogenic variation in the All of Us cohort reveals ancestry-driven disparities

Eric Venner, Karynne Patterson, Divya Kalra, Marsha M. Wheeler, Yi–Ju Chen, Sara E. Kalla, Bo Yuan, Jason H. Karnes, Kimberly Walker, Joshua D. Smith, Sean R. McGee, Aparna Radhakrishnan, Andrew Haddad, Philip E. Empey, Qiaoyan Wang, , May Okihiro

The paper analyzes pathogenic variant frequencies in the All of Us cohort by ancestry, revealing disparities and potential biases in genomic data.

Communications Biology · Feb 19, 2024Peer-reviewedGenetic & Genomic Medicine