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Magda Mielczarek

As seen in: MDPI, BioRxiv
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Articles

An Explainable Deep Learning Classifier of Bovine Mastitis Based on Whole-Genome Sequence Data-Circumventing the p >> n Problem

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By Krzysztof Kotlarz, Magda Mielczarek, Przemysław Biecek, Katarzyna Wojdak-Maksymiec
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Exploring the impact of sequence context on errors in SNP genotype calling with Whole Genome Sequencing data using AI-based autoencoder approach

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By Krzysztof Kotlarz, Magda Mielczarek, Przemyslaw Biecek, Bernt Guldbrandtsen
| BioRxiv
Abstract A critical step in the analysis of WGS data is variant calling. Despite its importance, variant calling is prone to errors. Our study investigated the association between incorrect SNP and variant quality metrics and nucleotide context. In our study, incorrect SNPs were defined in twenty Holstein-Friesian cows by comparing their SNPs genotypes identified by whole genome sequencing with the IlluminaNovaSeq6000 and the EuroGMD50K genotyping microarray.
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Exploring the distribution of single nucleotide polymorphisms across human exons and introns

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By Jakub Liu, Magda Mielczarek, Paula Dobosz, Magdalena Fraszczak
| BioRxiv
Abstract Among all types of mutations, single nucleotide polymorphisms are the most common type of genomic variation. In our study, we explore the counts of single nucleotide polymorphisms in particular exons and introns of the human genome based on the data set of 1,222 individuals of Polish origin that comprises 41,836,187 polymorphisms.
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