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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Identifying Subgroups of Patients With Autism by Gene Expression Profiles Using Machine Learning Algorithms.

Ping-I Lin1,2, Mohammad Ali Moni1, Susan Shur-Fen Gau3

  • 1School of Psychiatry, The University of New South Wales, Sydney, NSW, Australia.

Frontiers in Psychiatry
|May 31, 2021
PubMed
Summary

Machine learning identified autism spectrum disorder (ASD) subgroups with distinct clinical features. These subgroups, linked to social communication deficits and cholesterol metabolism, can be predicted with over 90% accuracy.

Keywords:
autism spectrum disordergenomicslanguagemachine learningsocial cognition

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Area of Science:

  • Genomics
  • Computational Biology
  • Neuroscience

Background:

  • Autism spectrum disorder (ASD) presents significant clinical heterogeneity, complicating management.
  • Identifying homogeneous subgroups is crucial for improving clinical outcomes and treatment strategies.

Purpose of the Study:

  • To apply machine learning algorithms to microarray data for identifying ASD subgroups with homogeneous clinical features.
  • To explore the role of specific gene expression pathways in social communication deficits within ASD.

Main Methods:

  • Whole-genome gene expression microarray data analyzed to identify differential expression regions (DERs).
  • Gene set enrichment analysis performed on DERs to identify key pathways.
  • Random Forest (RF) and Support Vector Machine (SVM) used to classify ASD subgroups based on DERs.

Main Results:

  • 191 DERs identified, with 54 selected for pathway analysis.
  • Cholesterol biosynthesis and metabolism pathways identified as hubs influencing social communication deficits in ASD.
  • RF and SVM achieved >90% accuracy in classifying ASD subtypes, particularly those with language impairment.

Conclusions:

  • Machine learning algorithms like RF and SVM are effective for identifying clinically homogeneous ASD subgroups.
  • Transcriptomic profiles related to social communication and cholesterol metabolism can predict ASD subtypes and prognosis.