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Related Concept Videos

Autism Spectrum Disorder01:19

Autism Spectrum Disorder

104
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.
104

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Machine Learning Algorithms Applied to Predict Autism Spectrum Disorder Based on Gut Microbiome Composition.

Juan M Olaguez-Gonzalez1, Isaac Chairez1,2, Luz Breton-Deval3,4

  • 1School of Engineering and Science, Tecnologico de Monterrey, Monterrey 64849, Mexico.

Biomedicines
|October 28, 2023
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Summary
This summary is machine-generated.

Machine learning models accurately diagnose autism spectrum disorder (ASD) by analyzing gut microbiome composition. Focusing on less abundant bacteria, this study explains contradictory findings and improves early ASD detection accuracy up to 90%.

Keywords:
ASDartificial neural networksautismmachine learningmicrobiomemicrobiota

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Area of Science:

  • Computational biology and bioinformatics
  • Microbiome research and analysis
  • Neurodevelopmental disorder diagnostics

Background:

  • Gut microbiome composition is increasingly linked to autism spectrum disorder (ASD), but previous research yielded contradictory results.
  • Existing studies often overlook less abundant microbial communities, potentially missing key diagnostic indicators.
  • Machine learning (ML) offers a powerful approach to analyze complex biological data for disease diagnosis.

Purpose of the Study:

  • To investigate the role of gut microbiome composition in the early diagnosis of ASD using machine learning.
  • To develop and validate ML models for classifying individuals as neurotypical (NT) or having ASD based on microbiome data.
  • To identify key microbial predictors associated with ASD and explain discrepancies in prior research.

Main Methods:

  • Applied machine learning algorithms including Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Random Forest (RF) for classification.
  • Utilized published gut microbiome composition data (16S rRNA sequencing) from two independent datasets (USA and China).
  • Trained and validated multiple ML models, focusing on those with the best performance and interpretability.

Main Results:

  • Achieved high classification accuracy (up to 90%) with excellent sensitivity (96.97%) and specificity (85.29%) for ASD detection.
  • ANN models demonstrated perfect classification of neurotypical subjects in one dataset, highlighting significant diagnostic potential.
  • Identified key bacterial predictors such as *Bacteroides*, *Lachnospira*, *Anaerobutyricum*, and *Ruminococcus torques*, including previously overlooked low-abundance microbes.

Conclusions:

  • Machine learning models effectively utilize gut microbiome data for accurate ASD diagnosis, offering an improvement over traditional methods.
  • The study suggests that less abundant microbial communities play a crucial role in ASD development and diagnosis, explaining conflicting prior findings.
  • Further research into these minority microbial populations is recommended to refine understanding and diagnostic capabilities for ASD.