Author

Piotr Dworzyński

Statens Serum InstitutORCID 0000-0002-6717-8052Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Research Focus:differential expression· 1 Assay:electronic health records· 1 Research Focus:GWAS / association mapping· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Assay:metabolomics· 1 Approach:statistical model· 1

Frequent Co-Authors

Asker BrejnrodiD· 1 shared Henrik HjalgrimiD· 1 shared Justin J. J. van der HooftiD· 1 shared Klaus RostgaardiD· 1 shared Lasse Buur Rasmussen· 1 shared Madeleine ErnstiD· 1 shared Mads MelbyeiD· 1 shared Manimozhiyan ArumugamiD· 1 shared

Activity by Year

2019-2020 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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1 shared paperBioinformatics

Asker BrejnrodORCID

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2 papers

Recent·Most cited

Nationwide prediction of type 2 diabetes comorbidities

Piotr Dworzyński, Martin Aasbrenn, Klaus Rostgaard, Mads Melbye, Thomas Alexander Gerds, Henrik Hjalgrim, 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.

Scientific Reports · Feb 4, 2020Peer-reviewedBioinformatics

Implementations of the chemical structural and compositional similarity metric in R and Python

Asker Brejnrod, Madeleine Ernst, Piotr Dworzyński, Lasse Buur Rasmussen, Pieter C. Dorrestein, Justin J. J. van der Hooft, Manimozhiyan Arumugam

The paper provides efficient implementations of the Chemical Structural and Compositional Similarity metric in R and Python, significantly improving speed and usability for metabolomics analysis.

bioRxiv · Feb 11, 2019 · PreprintBioinformatics