Article

Identification of biological mechanisms underlying a multidimensional ASD phenotype using machine learning

2018-11-16

Abstract excerpt

The complex genetic architecture of Autism Spectrum Disorder (ASD) and its heterogeneous phenotype make molecular diagnosis and patient prognosis challenging tasks. To establish more precise genotype-phenotype correlations in ASD, we developed a novel machine learning integrative approach, which seeks to delineate associations between patients’ clinical profiles and disrupted biological processes inferred from the...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 4395628d-b2fd-54c5-9af5-f3d07b4cc7a7
DOI: 10.1101/470757

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Identification of biological mechanisms underlying a multidimensional ASD phenotype using machine learningDOI 10.1101/470757Read

Identification of biological mechanisms underlying a multidimensional ASD phenotype using machine learning.Semantic proximity Read

Machine learning of clinical phenotypes facilitates autism screening and identifies novel subgroups with distinct transcriptomic profiles.Semantic proximity Read

Genome-wide characterization of genetic and functional dysregulation in autism spectrum disorderSemantic proximity Read

Novel clinical, molecular and bioinformatics insights into the genetic background of autism.Semantic proximity Read

Enrichment of genomic variation in pathways linked to autismSemantic proximity Read

Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder.Semantic proximity Read

A novel data-driven approach reveals gene networks and biological processes underlying autismSemantic proximity Read

A machine learning approach to predicting autism risk genes: Validation of known genes and discovery of new candidatesSemantic proximity Read

Network- and attribute-based classifiers can prioritize genes and pathways for autism spectrum disorders and intellectual disability.Semantic proximity Read

Calculating the Effects of Autism Risk Gene Variants on Dysfunction of Biological Processes Identifies Clinically-Useful InformationSemantic proximity Read

Transcriptomic analysis in autism spectrum disorder suggests three molecular subtypes with distinct phenotypic profiles and functional pathways.Semantic proximity Read

Genetic architecture of autism spectrum disorder: Lessons from large-scale genomic studies.Semantic proximity Read