Author

Jennifer M.S. Koh

The University of SydneyORCID 0000-0003-2140-3151Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Organism:human· 2 Assay:proteomics· 2 Research Focus:cancer & tumor microenvironment· 1 Research Focus:drug response / drug discovery· 1

Frequent Co-Authors

Dylan XavieriD· 2 shared Karen L. MacKenzieiD· 2 shared Michael DausmanniD· 2 shared Natasha LucasiD· 2 shared Peter G. HainsiD· 2 shared Phillip James RobinsoniD· 2 shared Qing ZhongiD· 2 shared Rebecca C. PoulosiD· 2 shared

Activity by Year

2022-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersCancer Biology

Dylan XavierORCID

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2 papers

Recent·Most cited

Federated deep learning enables cancer subtyping by proteomics

Zhaoxiang Cai, Emma L. Boys, Zainab Noor, Adel T. Aref, Dylan Xavier, Natasha Lucas, Steven G. Williams, Jennifer M.S. Koh, Rebecca C. Poulos, Yangxiu Wu, Michael Dausmann, Karen L. MacKenzie, Adriana Aguilar‐Mahecha, Carolina Armengol, Maria Manuela Barranco, , Roger R. Reddel

ProCanFDL uses federated deep learning to subtype cancers using proteomic data while preserving privacy, achieving performance gains over local models.

Cancer Discovery · Jun 9, 2025Peer-reviewedCancer Biology

Pan-cancer proteomic map of 949 human cell lines reveals principles of cancer vulnerabilities

Emanuel Gonçalves, Rebecca C. Poulos, Zhaoxiang Cai, Syd Barthorpe, Srikanth Srinivas Manda, Natasha Lucas, Alexandra Beck, Daniel Bucio-Noble, Michael Dausmann, Caitlin Hall, Michael Hecker, Jennifer M.S. Koh, Howard W. Lightfoot, Sadia Mahboob, Iman D. Mali, , Roger R. Reddel

The paper presents a pan-cancer proteomic map of 949 cell lines, revealing protein-specific biomarkers of cancer vulnerabilities and showing proteomic data's superior predictive power for drug response.

Cancer Cell · Jul 14, 2022Peer-reviewedSystems Biology