biostudies-literatureRakowski AHelmholtz Einstein International Berlin Research School in Data ScienceDeutsche ForschungsgemeinschaftHPI Research School on Data Science and EngineeringEuropean Commissionii4-ii10https://www.ebi.ac.uk/biostudies/studies/S-EPMC11373386biostudies-literatureUnknown40(Suppl 2)With the development of high-throughput technologies, genomics datasets rapidly grow in size, including functional genomics data. This has allowed the training of large Deep Learning (DL) models to predict epigenetic readouts, such as protein binding or histone modifications, from genome sequences. However, large dataset sizes come at a price of data consistency, often aggregating results from a large number of studies, conducted under varying experimental conditions. While data from large-scale consortia are useful as they allow studying the effects of different biological conditions, they can also contain unwanted biases from confounding experimental factors. Here, we introduce Metadata-guided Feature Disentanglement (MFD)-an approach that allows disentangling biologically relevant featuBioinformatics (Oxford, England)Metadata-guided feature disentanglement for functional genomics.PMC11373386RE3474/8–1LI 3333/5-1101016775GR 3793/6–1LI 3333/5–1OH 266/6–1Ohler URakowski ALippert CHuryn VLemanczyk MMonti RfalseMetadata-guided feature disentanglement for functional genomics.With the development of high-throughput technologies, genomics datasets rapidly grow in size, including functional genomics data. This has allowed the training of large Deep Learning (DL) models to predict epigenetic readouts, such as protein binding or histone modifications, from genome sequences. However, large dataset sizes come at a price of data consistency, often aggregating results from a large number of studies, conducted under varying experimental conditions. While data from large-scale consortia are useful as they allow studying the effects of different biological conditions, they can also contain unwanted biases from confounding experimental factors. Here, we introduce Metadata-guided Feature Disentanglement (MFD)-an approach that allows disentangling biologically relevant featu2024-01-01T00:00:00Z2024 Sep2026-06-01T16:31:18.495Z2025-04-05T22:13:48.307ZS-EPMC113733863923070010.1093/bioinformatics/btae403