Machine learning models, and training, validation and test datasets for: "Sequence determinants of human gene regulatory elements"

dc.contributor.affiliationApplied Tumor Genomics Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland. Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden. Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom-Taipale, Jussi
dc.contributor.authorTaipale, Jussi
dc.date.accessioned2025-04-29T14:04:24Z
dc.date.issued2021-07-16
dc.date.issued2021-07-16
dc.descriptionThis record contains the training, test and validation datasets used to train and evaluate the machine learning models in manuscript: Sahu, Biswajyoti, et al. "Sequence determinants of human gene regulatory elements." (2021). This record contains also the final hyperparameter-optimized models for each training dataset/task combination described in the manuscript. The README-files provided with the record describe the datasets and models in more detail. The datasets deposited here are derived from the original raw data (GEO accession: GSE180158) as described in the Methods of the manuscript.
dc.identifierhttps://doi.org/10.5281/zenodo.5101420
dc.identifier.urihttps://datakatalogi.helsinki.fi/handle/123456789/5843
dc.rights.licensecc-by-4.0
dc.subjectGene regulation
dc.subjectSTARR-seq
dc.subjectDeep learning
dc.subjectConvolutional Neural Networks
dc.subjectMachine learning
dc.titleMachine learning models, and training, validation and test datasets for: "Sequence determinants of human gene regulatory elements"
dc.typedataset
Zenodo


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