Author

Piotr Jaroslaw Chmura

University of CopenhagenORCID 0000-0002-9371-6918Unclaimed 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 Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Assay:electronic health records· 1 Research Focus:gene regulatory network inference· 1 Approach:statistical model· 1

Frequent Co-Authors

Søren BrunakiD· 2 shared Alexander Chowdhury· 1 shared Alexandra Franz· 1 shared Amalie Dahl HaueiD· 1 shared Aviv RegeviD· 1 shared Aylwyn ScallyiD· 1 shared Bitten Aagaard Jensen· 1 shared Bo YuaniD· 1 shared

Activity by Year

2021-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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2 shared papersGenetics

Søren BrunakORCID

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Author profile →

2 papers

Recent·Most cited

Population-scale inheritance analysis of 858,635 individuals reveals North Sea migration from the Middle Ages to the Industrial Revolution

Xiaolei Zhang, Poul Holm, Aylwyn Scally, Karina Banasik, Laust Hvas Mortensen, Tomas W. Fitzgerald, David Westergaard, Piotr Jaroslaw Chmura, Sisse Rye Ostrowski, Erik Sørensen, Ole Birger Pedersen, Michael Schwinn, Christian Erikstrup, Mie Topholm Bruun, Bitten Aagaard Jensen, , Ewan Birney

The paper uses genetic data from Denmark and Britain to infer historical North Sea migrations from the Middle Ages to the Industrial Revolution, identifying distinct population patterns and migration trends.

bioRxiv · Mar 19, 2025 · PreprintGenetics

Pancreatic cancer risk predicted from disease trajectories using deep learning

Davide Placido, Bo Yuan, Jessica Xin Hjaltelin, Chunlei Zheng, Amalie Dahl Haue, Piotr Jaroslaw Chmura, Chen Yuan, Jihye Kim, Renato Umeton, Gregory C. Antell, Alexander Chowdhury, Alexandra Franz, Lauren K. Brais, Elizabeth Andrews, Debora S. Marks, , Chris Sander

This paper uses deep learning to predict pancreatic cancer risk from clinical disease trajectories in two large datasets, achieving higher accuracy than models without temporal information.

bioRxiv · Jun 27, 2021 · PreprintBioinformatics