Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

A Bayesian framework to integrate multi-level genome-scale data for Autism risk gene prioritization.

BMC bioinformatics - 22 Apr 2022

Ji Ying, Chen Rui, Wang Quan, Wei Qiang, Tao Ran, Li Bingshan

Abstract excerpt

BACKGROUND: Autism spectrum disorder (ASD) is a group of complex neurodevelopment disorders with a strong genetic basis. Large scale sequencing studies have identified over one hundred ASD risk genes. Nevertheless, the vast majority of ASD risk genes remain to be discovered, as it is estimated that more than 1000 genes are likely to be involved in ASD risk. Prioritization of risk genes is an effective strategy to...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Autism Spectrum Disorder
Share to Autistic Disorder
Share to Bayes Theorem

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Gene Regulatory Networks

Share to Genetic Predisposition to Disease

Share to Genome-Wide Association Study

Source

Open in PubMed Open full text

Identifiers

PMID: 35459094
DOI: 10.1186/s12859-022-04616-y
PMCID: PMC9034518

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

A Bayesian framework to integrate multi-level genome-scale data for Autism risk gene prioritization.PMID 35459094Read ↗

Brain-specific functional relationship networks inform autism spectrum disorder gene prediction.Semantic proximity Read ↗

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

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

Integrated Bayesian analysis of rare exonic variants to identify risk genes for schizophrenia and neurodevelopmental disorders.Semantic proximity Read ↗

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

Forecasting risk gene discovery in autism with machine learning and genome-scale data.Semantic proximity Read ↗

ASiDentify (ASiD): a machine learning model to predict new autism spectrum disorder risk genes.Semantic proximity Read ↗

Prioritizing de novo autism risk variants with calibrated gene- and variant-scoring models.Semantic proximity Read ↗

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

A Novel Phenotype-based Approach for Prioritizing Candidate Genetic Variants for Autism Spectrum DisorderSemantic proximity Read ↗

Can machine learning aid in identifying disease genes? The case of autism spectrum disorderSemantic proximity Read ↗

Genome-wide prediction of dominant and recessive neurodevelopmental disorder risk genesSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.