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Détail de l'auteur
Auteur Julie C. CHOW |
Documents disponibles écrits par cet auteur (1)
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Prediction of Neurodevelopmental Disorders Based on De Novo Coding Variation / Julie C. CHOW in Journal of Autism and Developmental Disorders, 53-3 (March 2023)
[article]
Titre : Prediction of Neurodevelopmental Disorders Based on De Novo Coding Variation Type de document : Texte imprimé et/ou numérique Auteurs : Julie C. CHOW, Auteur ; Fereydoun HORMOZDIARI, Auteur Article en page(s) : p.963-976 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : The early detection of neurodevelopmental disorders (NDDs) can significantly improve patient outcomes. The differential burden of non-synonymous de novo mutation among NDD cases and controls indicates that de novo coding variation can be used to identify a subset of samples that will likely display an NDD phenotype. Thus, we have developed an approach for the accurate prediction of NDDs with very low false positive rate (FPR) using de novo coding variation for a small subset of cases. We use a shallow neural network that integrates de novo likely gene-disruptive and missense variants, measures of gene constraint, and conservation information to predict a small subset of NDD cases at very low FPR and prioritizes NDD risk genes for future clinical study. En ligne : https://doi.org/10.1007/s10803-022-05586-z Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=500
in Journal of Autism and Developmental Disorders > 53-3 (March 2023) . - p.963-976[article] Prediction of Neurodevelopmental Disorders Based on De Novo Coding Variation [Texte imprimé et/ou numérique] / Julie C. CHOW, Auteur ; Fereydoun HORMOZDIARI, Auteur . - p.963-976.
Langues : Anglais (eng)
in Journal of Autism and Developmental Disorders > 53-3 (March 2023) . - p.963-976
Index. décimale : PER Périodiques Résumé : The early detection of neurodevelopmental disorders (NDDs) can significantly improve patient outcomes. The differential burden of non-synonymous de novo mutation among NDD cases and controls indicates that de novo coding variation can be used to identify a subset of samples that will likely display an NDD phenotype. Thus, we have developed an approach for the accurate prediction of NDDs with very low false positive rate (FPR) using de novo coding variation for a small subset of cases. We use a shallow neural network that integrates de novo likely gene-disruptive and missense variants, measures of gene constraint, and conservation information to predict a small subset of NDD cases at very low FPR and prioritizes NDD risk genes for future clinical study. En ligne : https://doi.org/10.1007/s10803-022-05586-z Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=500