Author

Amalie Dahl Haue

University of CopenhagenORCID 0000-0001-7656-7976Unclaimed 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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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:GWAS / association mapping· 1 Assay:GWAS summary statistics· 1

Frequent Co-Authors

Søren BrunakiD· 2 shared Alexander Chowdhury· 1 shared Alexander Pil HenrikseniD· 1 shared Alexandra Franz· 1 shared Aviv RegeviD· 1 shared Bo YuaniD· 1 shared Brian Matthew WolpiniD· 1 shared Chen 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 papersGenetic & Genomic Medicine

Søren BrunakORCID

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2 papers

Recent·Most cited

Genome-wide associations spanning 194 in-hospital drug dosage change phenotypes highlight diverse genetic backgrounds in concurrent drug therapy

Alexander Pil Henriksen, Cristina Leal Rodríguez, Hannah Currant, Ioannis Louloudis, Jorge Hernansanz Biel, María Herrero-Zazo, Ewan Birney, Thomas Folkmann Hansen, Gianluca Mazzoni, Amalie Dahl Haue, Henning Bundgaard, Christian Erikstrup, Khoa Manh Dinh, Liam James Elgaard Quinn, Mie Topholm Bruun, , Søren Brunak

This study identifies 51 genome-wide significant loci associated with dosage changes in 42 drug pair phenotypes, revealing distinct genetic patterns in polypharmacy.

Computational and Structural Biotechnology Journal · Jan 1, 2025Peer-reviewedGenetic & Genomic Medicine

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