Author

Przemysław Biecek

Warsaw University of TechnologyORCID 0000-0001-8423-1823Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Assay:whole-genome sequencing· 2 Approach:deep learning· 1 Research Focus:variant calling· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Joanna SzydaiD· 2 shared Krzysztof KotlarziD· 2 shared Magda MielczarekiD· 2 shared Bernt GuldbrandtseniD· 1 shared Katarzyna Wojdak-MaksymieciD· 1 shared Piotr TopolskiiD· 1 shared Tomasz SuchockiiD· 1 shared W. JagusiakiD· 1 shared

Activity by Year

2024-2024 · peak 2/yr

Co-Author Collaboration Network

8 top collaborators · 24 mutual co-author links

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2 shared papersBioinformatics

Joanna SzydaORCID

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2 papers

Recent·Most cited

BioinformaticsNAR Genomics and BioinformaticsPeer-reviewedJul 2, 2024

Exploring the impact of sequence context on errors in SNP genotype calling with Whole Genome Sequencing data using AI-based autoencoder approach

Krzysztof Kotlarz, Magda Mielczarek, Przemysław Biecek…·Joanna Szyda

The paper uses an autoencoder to identify systematic errors in SNP genotype calling from WGS data by analyzing sequence context and quality metrics.

4 citations

whole-genome sequencingstatistical model

𝕏 PostBluesky

BioinformaticsInternational Journal of Molecular SciencesPeer-reviewedApr 26, 2024

An explainable deep learning classifier of bovine mastitis based on whole genome sequence data - circumventing the p>>>n problem

Krzysztof Kotlarz, Magda Mielczarek, Przemysław Biecek…·Joanna Szyda

The paper develops a deep learning classifier for bovine mastitis susceptibility using whole genome sequence data, overcoming the p>>n problem through LASSO and deep learning integration.

10 citations

whole-genome sequencingstatistical model

𝕏 PostBluesky