Author

Louisa-Marie Krützfeldt

Berlin Institute of Health at Charité - Universitätsmedizin Berlin

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Bioinformatics· 1

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Research Focus:benchmarking· 1 Assay:ChIP-seq· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Assay:DNA methylation· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Approach:machine learning (classical)· 1

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Martin KircheriD· 1 shared Max SchubachiD· 1 shared

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Martin KircherORCID

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Recent·Most cited

BioinformaticsPLoS ONEPeer-reviewedDec 1, 2020

The impact of different negative training data on regulatory sequence predictions

Louisa-Marie Krützfeldt, Max Schubach, Martin Kircher

This study evaluates how different negative training data affect regulatory sequence prediction models, finding that genomic backgrounds yield better results than shuffled sequences.

13 citations

humanDNA methylation

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