Clinically relevant single gene or intragenic deletions encompassing critical neurodevelopmental genes in patients with developmental delay, mental retardation, and/or autism spectrum disorders

2011 ◽  
Vol 155 (10) ◽  
pp. 2386-2396 ◽  
Author(s):  
Fady M. Mikhail ◽  
Edward J. Lose ◽  
Nathaniel H. Robin ◽  
Maria D. Descartes ◽  
Katherine D. Rutledge ◽  
...  
Keyword(s):  
Mental Retardation ◽  
Autism Spectrum Disorders ◽  
Developmental Delay ◽  
Single Gene ◽  
Autism Spectrum ◽  
Intragenic Deletions ◽  
Spectrum Disorders

scholarly journals Dopaminergic Gene Dosage in Autism versus Developmental Delay: From Complex Networks to Machine Learning approaches

2020 ◽  
Author(s):  
André Santos ◽  
Francisco Caramelo ◽  
Joana Barbosa de Melo ◽  
Miguel Castelo-Branco
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorders ◽  
Network Analysis ◽  
Developmental Delay ◽  
Gene Dosage ◽  
Genetic Alterations ◽  
Autism Spectrum ◽  
Compact Set ◽  
Neural Basis ◽  
Spectrum Disorders

AbstractThe neural basis of behavioural changes in Autism Spectrum Disorders (ASD) remains a controversial issue. One factor contributing to this challenge is the phenotypic heterogeneity observed in ASD, which suggests that several different system disruptions may contribute to diverse patterns of impairment between and within study samples. Here, we took a retrospective approach, using SFARI data to study ASD by focusing on participants with genetic imbalances targeting the dopaminergic system. Using complex network analysis, we investigated the relations between participants, Gene Ontology (GO) and gene dosage related to dopaminergic neurotransmission from a polygenic point of view. We converted network analysis into a machine learning binary classification problem to differentiate ASD diagnosed participants from DD (developmental delay) diagnosed participants. Using 1846 participants to train a Random Forest algorithm, our best classifier achieved on average a diagnosis predicting accuracy of 85.18% (sd 1.11%) on a test sample of 790 participants using gene dosage features. In addition, we observed that if the classifier uses GO features it was also able to infer a correct response based on the previous examples because it is tied to a set of biological process, molecular functions and cellular components relevant to the problem. This yields a less variable and more compact set of features when comparing with gene dosage classifiers. Other facets of knowledge-based systems approaches addressing ASD through network analysis and machine learning, providing an interesting avenue of research for the future, are presented through the study.Lay SummaryThere are important issues in the differential diagnosis of Autism Spectrum Disorders. Gene dosage effects may be important in this context. In this work, we studied genetic alterations related to dopamine processes that could impact brain development and function of 2636 participants. On average, from a genetic sample we were able to correctly separate autism from developmental delay with an accuracy of 85%.

scholarly journals Proteomics and Metabolomics Approaches towards a Functional Insight onto AUTISM Spectrum Disorders: Phenotype Stratification and Biomarker Discovery

2020 ◽  
Vol 21 (17) ◽  
pp. 6274
Author(s):  
Maria Vittoria Ristori ◽  
Stefano Levi Mortera ◽  
Valeria Marzano ◽  
Silvia Guerrera ◽  
Pamela Vernocchi ◽  
...  
Keyword(s):  
Autism Spectrum Disorders ◽  
Biomarker Discovery ◽  
Chromosomal Abnormalities ◽  
Single Gene ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Autism Spectrum ◽  
Added Value ◽  
Diagnostic Tools ◽  
Spectrum Disorders

Autism spectrum disorders (ASDs) are neurodevelopmental disorders characterized by behavioral alterations and currently affect about 1% of children. Significant genetic factors and mechanisms underline the causation of ASD. Indeed, many affected individuals are diagnosed with chromosomal abnormalities, submicroscopic deletions or duplications, single-gene disorders or variants. However, a range of metabolic abnormalities has been highlighted in many patients, by identifying biofluid metabolome and proteome profiles potentially usable as ASD biomarkers. Indeed, next-generation sequencing and other omics platforms, including proteomics and metabolomics, have uncovered early age disease biomarkers which may lead to novel diagnostic tools and treatment targets that may vary from patient to patient depending on the specific genomic and other omics findings. The progressive identification of new proteins and metabolites acting as biomarker candidates, combined with patient genetic and clinical data and environmental factors, including microbiota, would bring us towards advanced clinical decision support systems (CDSSs) assisted by machine learning models for advanced ASD-personalized medicine. Herein, we will discuss novel computational solutions to evaluate new proteome and metabolome ASD biomarker candidates, in terms of their recurrence in the reviewed literature and laboratory medicine feasibility. Moreover, the way to exploit CDSS, performed by artificial intelligence, is presented as an effective tool to integrate omics data to electronic health/medical records (EHR/EMR), hopefully acting as added value in the near future for the clinical management of ASD.

scholarly journals Cerebral Palsy, Autism Spectrum Disorders, and Developmental Delay in Children Born After Assisted Conception

2009 ◽  
Vol 163 (1) ◽  
pp. 72 ◽  
Author(s):  
Dorte Hvidtjørn ◽  
Laura Schieve ◽  
Diana Schendel ◽  
Bo Jacobsson ◽  
Claus Sværke ◽  
...  
Keyword(s):  
Cerebral Palsy ◽  
Autism Spectrum Disorders ◽  
Developmental Delay ◽  
Autism Spectrum ◽  
Assisted Conception ◽  
Spectrum Disorders

scholarly journals Assessment and Treatment in Autism Spectrum Disorders: A Focus on Genetics and Psychiatry

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Merlin G. Butler ◽  
Erin L. Youngs ◽  
Jennifer L. Roberts ◽  
Jessica A. Hellings
Keyword(s):  
Autism Spectrum Disorders ◽  
Chromosomal Abnormalities ◽  
Psychiatric Treatment ◽  
Single Gene ◽  
Challenging Behaviors ◽  
Autism Spectrum ◽  
Metabolic Disturbances ◽  
Dna Deletions ◽  
Assessment And Treatment ◽  
Spectrum Disorders

Autism spectrum disorders (ASDs) are neurobehavioral disorders characterized by abnormalities in three behavioral domains including social interaction, impaired communication, and repetitive stereotypic behaviors. ASD affects approximately 1% of children and is on the rise with significant genetic mechanisms underlying these disorders. We review the current understanding of the role of genetic and metabolic factors contributing to ASD with the use of new genetic technology. Fifty percent is diagnosed with chromosomal abnormalities, small DNA deletions/duplications, single-gene conditions, or metabolic disturbances. Genetic evaluation is discussed along with psychiatric treatment and approaches for selection of medication to treat associated challenging behaviors or comorbidities seen in ASD. We emphasize the importance of prioritizing treatment based on target symptom clusters and in what order for individuals with ASD, as the treatment may vary from patient to patient.

Autism Spectrum Disorders and Mental Retardation

2010 ◽  
pp. 186-192
Author(s):  
Jean-Michel Scherrmann ◽  
Kim Wolff ◽  
Christine A. Franco ◽  
Marc N. Potenza ◽  
Tayfun Uzbay ◽  
...  
Keyword(s):  
Mental Retardation ◽  
Autism Spectrum Disorders ◽  
Autism Spectrum ◽  
Spectrum Disorders
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