Publication: PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework
PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework
Date
Date
Date
Citations
Dingemans, A. J. M., Hinne, M., Truijen, K. M. G., Goltstein, L., van Reeuwijk, J., de Leeuw, N., Schuurs-Hoeijmakers, J., Pfundt, R., Diets, I. J., den Hoed, J., de Boer, E., Coenen-van der Spek, J., Jansen, S., van Bon, B. W., Jonis, N., Ockeloen, C. W., Vulto-van Silfhout, A. T., Kleefstra, T., Koolen, D. A., … et al. (2023). PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework. Nature Genetics, 55, 1598–1607. https://doi.org/10.1038/s41588-023-01469-w
Abstract
Abstract
Abstract
Several molecular and phenotypic algorithms exist that establish genotype–phenotype correlations, including facial recognition tools. However, no unified framework that investigates both facial data and other phenotypic data directly from individuals exists. We developed PhenoScore: an open-source, artificial intelligence-based phenomics framework, combining facial recognition technology with Human Phenotype Ontology data analysis to quantify phenotypic similarity. Here we show PhenoScore’s ability to recognize distinct phenotypic ent
Metrics
Downloads
Views
Additional indexing
Creators (Authors)
Volume
Volume
Volume
Number
Number
Number
Page range/Item number
Page range/Item number
Page range/Item number
Page end
Page end
Page end
Item Type
Item Type
Item Type
In collections
Dewey Decimal Classifikation
Dewey Decimal Classifikation
Dewey Decimal Classifikation
Keywords
Language
Language
Language
Publication date
Publication date
Publication date
Date available
Date available
Date available
ISSN or e-ISSN
ISSN or e-ISSN
ISSN or e-ISSN
Additional Information
Additional Information
Additional Information
OA Status
OA Status
OA Status
Publisher DOI
Metrics
Downloads
Views
Citations
Dingemans, A. J. M., Hinne, M., Truijen, K. M. G., Goltstein, L., van Reeuwijk, J., de Leeuw, N., Schuurs-Hoeijmakers, J., Pfundt, R., Diets, I. J., den Hoed, J., de Boer, E., Coenen-van der Spek, J., Jansen, S., van Bon, B. W., Jonis, N., Ockeloen, C. W., Vulto-van Silfhout, A. T., Kleefstra, T., Koolen, D. A., … et al. (2023). PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework. Nature Genetics, 55, 1598–1607. https://doi.org/10.1038/s41588-023-01469-w
