ArticleFrontiers in genetics2021
Interpretable Machine Learning Reveals Dissimilarities Between Subtypes of Autism Spectrum Disorder.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Generating Biologically Relevant Subtypes of Autism Spectrum Disorder with differential responses to Acute Oxytocin Administration in a Randomized Trial using Random Forest Models and K-means Clustering.medRxiv : the preprint server for health sciences · 2026Article
- SHAP-based explainable AI framework for autism severity classification using 3D motor biomarkers.Frontiers in psychiatry · 2026Article
- A Brief Observation to Screen Autism in Toddlers and Predict Developmental Trajectory.Journal of pediatrics. Clinical practice · 2025Article
- Autism Data Classification Using AI Algorithms with Rules: Focused Review.Bioengineering (Basel, Switzerland) · 2025Review
- Using Machine Learning Methods to Study Colorectal Cancer Tumor Micro-Environment and Its Biomarkers.International journal of molecular sciences · 2023Article
- A face image classification method of autistic children based on the two-phase transfer learning.Frontiers in psychology · 2023Article
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Authors and funding
7 authors.
Funding
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Abstract
Autism spectrum disorder (ASD) is a heterogeneous neuropsychiatric disorder with a complex genetic background. Analysis of altered molecular processes in ASD patients requires linear and nonlinear methods that provide interpretable solutions. Interpretable machine learning provides legible models that allow explaining biological mechanisms and support analysis of clinical subgroups. In this work, we investigated several case-control studies of gene expression measurements of ASD individuals. We constructed a rule-based learning model from three independent datasets that we further visualized as a nonlinear gene-gene co-predictive network. To find dissimilarities between ASD subtypes, we scrutinized a topological structure of the network and estimated a centrality distance. Our analysis revealed that autism is the most severe subtype of ASD, while pervasive developmental disorder-not otherwise specified and Asperger syndrome are closely related and milder ASD subtypes. Furthermore, we analyzed the most important ASD-related features that were described in terms of gene co-predictors. Among others, we found a strong co-predictive mechanism between
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