1 recherche sur le mot-clé 'Diagnostic model'

Single Nucleotide Polymorphisms Predict Symptom Severity of Autism Spectrum Disorder / Yun JIAO in Journal of Autism and Developmental Disorders, 42-6 (June 2012)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 42-6 (June 2012) . - p.971-983 Titre : Single Nucleotide Polymorphisms Predict Symptom Severity of Autism Spectrum Disorder Type de document : texte imprimé Auteurs : Yun JIAO, Auteur ; Rong CHEN, Auteur ; Xiaoyan KE, Auteur ; Lu CHENG, Auteur ; Kangkang CHU, Auteur ; Zuhong LU, Auteur ; Edward H. HERSKOVITS, Auteur Année de publication : 2012 Article en page(s) : p.971-983 Langues : Anglais (eng) Mots-clés : Autism-spectrum disorder  Single-nucleotide polymorphisms  Diagnostic model  Genotype-phenotype analysis  Data mining Index. décimale : PER Périodiques Résumé : Autism is widely believed to be a heterogeneous disorder; diagnosis is currently based solely on clinical criteria, although genetic, as well as environmental, influences are thought to be prominent factors in the etiology of most forms of autism. Our goal is to determine whether a predictive model based on single-nucleotide polymorphisms (SNPs) can predict symptom severity of autism spectrum disorder (ASD). We divided 118 ASD children into a mild/moderate autism group (n = 65) and a severe autism group (n = 53), based on the Childhood Autism Rating Scale (CARS). For each child, we obtained 29 SNPs of 9 ASD-related genes. To generate predictive models, we employed three machine-learning techniques: decision stumps (DSs), alternating decision trees (ADTrees), and FlexTrees. DS and FlexTree generated modestly better classifiers, with accuracy = 67%, sensitivity = 0.88 and specificity = 0.42. The SNP rs878960 in GABRB3 was selected by all models, and was related associated with CARS assessment. Our results suggest that SNPs have the potential to offer accurate classification of ASD symptom severity. En ligne : http://dx.doi.org/10.1007/s10803-011-1327-5 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=156 [article] Single Nucleotide Polymorphisms Predict Symptom Severity of Autism Spectrum Disorder [texte imprimé] / Yun JIAO, Auteur ; Rong CHEN, Auteur ; Xiaoyan KE, Auteur ; Lu CHENG, Auteur ; Kangkang CHU, Auteur ; Zuhong LU, Auteur ; Edward H. HERSKOVITS, Auteur . - 2012 . - p.971-983. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 42-6 (June 2012) . - p.971-983 Mots-clés : Autism-spectrum disorder  Single-nucleotide polymorphisms  Diagnostic model  Genotype-phenotype analysis  Data mining Index. décimale : PER Périodiques Résumé : Autism is widely believed to be a heterogeneous disorder; diagnosis is currently based solely on clinical criteria, although genetic, as well as environmental, influences are thought to be prominent factors in the etiology of most forms of autism. Our goal is to determine whether a predictive model based on single-nucleotide polymorphisms (SNPs) can predict symptom severity of autism spectrum disorder (ASD). We divided 118 ASD children into a mild/moderate autism group (n = 65) and a severe autism group (n = 53), based on the Childhood Autism Rating Scale (CARS). For each child, we obtained 29 SNPs of 9 ASD-related genes. To generate predictive models, we employed three machine-learning techniques: decision stumps (DSs), alternating decision trees (ADTrees), and FlexTrees. DS and FlexTree generated modestly better classifiers, with accuracy = 67%, sensitivity = 0.88 and specificity = 0.42. The SNP rs878960 in GABRB3 was selected by all models, and was related associated with CARS assessment. Our results suggest that SNPs have the potential to offer accurate classification of ASD symptom severity. En ligne : http://dx.doi.org/10.1007/s10803-011-1327-5 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=156