Author

Klaus Rostgaard

Statens Serum InstitutORCID 0000-0001-6220-9414Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Genetic & Genomic Medicine· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Research Focus:variant effect prediction· 3 Research Focus:benchmarking· 2 Assay:electronic health records· 2 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:GWAS / association mapping· 1 Assay:GWAS summary statistics· 1

Frequent Co-Authors

Henrik HjalgrimiD· 3 shared Bertram Dalskov KjerulffiD· 2 shared Christian ErikstrupiD· 2 shared Christina MikkelseniD· 2 shared Daniel Fannar GudbjartssoniD· 2 shared David WestergaardiD· 2 shared Erik SørenseniD· 2 shared Henrik UllumiD· 2 shared

Activity by Year

2020-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersBioinformatics

Henrik HjalgrimORCID

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3 papers

Recent·Most cited

Genetic & Genomic MedicineBritish Journal of CancerPeer-reviewedJul 14, 2026

Combining genome-wide polygenic scores with registry data for colorectal cancer risk-based screening

Anne Krogh Nøhr, Marie Giehm Overby, Mads Munk Nielsen…·Steffen UIlltz Thorsen

This study evaluates polygenic risk scores (PRS) for colorectal cancer (CRC) screening, showing they stratify risk independently of ancestry and sex but offer limited additional predictive value when combined with FIT.

DOI 10.1038/s41416-026-03532-9

humanwhole-exome sequencing

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BioinformaticsNucleic Acids ResearchPeer-reviewedMay 24, 2023

Deep integrative models for large-scale human genomics

Arnor Ingi Sigurdsson, Ioannis Evangelos Louloudis, Karina Banasik…·Simon Rasmussen

The paper introduces a deep learning framework for polygenic risk score prediction, showing improved performance over traditional methods for various diseases.

36 citationstop 3%

humanGWAS summary statistics

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BioinformaticsScientific ReportsPeer-reviewedFeb 4, 2020

Nationwide prediction of type 2 diabetes comorbidities

Piotr Dworzyński, Martin Aasbrenn, Klaus Rostgaard…·Tune H. Pers

This study uses machine learning on Danish health registers to predict five-year risk of T2D comorbidities, showing modest improvements over reference models.

58 citationstop 2%

humanelectronic health records

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