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Related Concept Videos

Autism Spectrum Disorder01:19

Autism Spectrum Disorder

167
Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.
167

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Differential Gene Expression Data Analysis of ASD Using Random Forest.

Pragya1, Praveen Kumar Govarthan1, Kshitij Sinha2

  • 1School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India.

Studies in Health Technology and Informatics
|May 19, 2023
PubMed
Summary
This summary is machine-generated.

This study uses machine learning to identify key gene expression differences in autism spectrum disorder (ASD). The findings highlight 10 gene signatures and a specific chromosomal location, aiding in biomarker discovery for ASD diagnosis.

Keywords:
Autism Spectrum DisorderGene expression dataNCBIRandom ForestStatistical test

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Area of Science:

  • Genomics
  • Bioinformatics
  • Neuroscience

Background:

  • Autism spectrum disorder (ASD) is a neurodevelopmental condition linked to brain differences.
  • Transcriptomic analysis of differential gene expression (DE) is crucial for understanding ASD-related genetic changes.
  • Identifying reliable biomarkers for ASD diagnosis remains a significant challenge.

Purpose of the Study:

  • To apply a machine learning approach to identify differential gene expression between individuals with ASD and typical development (TD).
  • To discover potential gene signatures and chromosomal locations that can serve as biomarkers for ASD.

Main Methods:

  • Utilized transcriptomic data from 15 ASD and 15 TD individuals obtained from the NCBI GEO database.
  • Employed a Random Forest (RF) machine learning model for gene discrimination.
  • Performed data pre-processing and analyzed differential gene expression (DEG) and chromosomal locations.

Main Results:

  • The RF model achieved high performance with 96.67% accuracy, sensitivity, and specificity.
  • Identified the top 10 prominent differentially expressed genes (DEGs) and 34 unique DEG chromosomal locations.
  • Pinpointed chr3:113322718-113322659 as the most significant chromosomal location for ASD discrimination.

Conclusions:

  • The machine learning-based DE analysis is effective for identifying ASD biomarkers from gene expression profiles.
  • The identified top 10 gene signatures and significant chromosomal location offer potential for developing reliable ASD diagnostic and prognostic tools.