ArticleClinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology2017
Predicting Autism Spectrum Disorder Using Blood-based Gene Expression Signatures and Machine Learning.
Article in Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 52 citations in OpenAlex.
- Mitochondrial-Epigenetic Crosstalk in Autism Spectrum Disorder: Linking Cellular Stress to Synaptic Dysfunction and Treatment Resistance.Molecular neurobiology · 2026Review
- A joint complex network and machine learning approach for the identification of discriminative gene communities in autistic brain.PloS one · 2025Article
- Expression assay of calcium signaling related lncRNAs in autism.Molecular biology reports · 2024Article
- Machine Learning Algorithms for the Prediction of Locomotor Activity by an Infrared Motion Detector on the Sleep-wake States in Mice.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2023Article
- Machine Learning Data Analysis Highlights the Role ofBiomedicines · 2022Article
- Why not try to predict autism spectrum disorder with crucial biomarkers in cuproptosis signaling pathway?Frontiers in psychiatry · 2022Article
- Effects of the Prenatal Administration of Tetanus Toxoid on the Sociability and Explorative Behaviors of Rat Offspring: A Preliminary Study.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2021Article
- Interpretable Machine Learning Reveals Dissimilarities Between Subtypes of Autism Spectrum Disorder.Frontiers in genetics · 2021Article
- Comprehensive Analysis of RNA-Seq Gene Expression Profiling of Brain Transcriptomes Reveals Novel Genes, Regulators, and Pathways in Autism Spectrum Disorder.Brain sciences · 2020Article
- Towards a Multivariate Biomarker-Based Diagnosis of Autism Spectrum Disorder: Review and Discussion of Recent Advancements.Seminars in pediatric neurology · 2020Review
- Cell Type-Specific Predictive Models Perform Prioritization of Genes and Gene Sets Associated With Autism.Frontiers in genetics · 2020Article
- Biomarkers for Autism Spectrum Disorders (ASD): A Meta-analysis.Rambam Maimonides medical journal · 2019Article
- A cross-talk between blood-cell neuroplasticity-related genes and environmental enrichment in working dogs.Scientific reports · 2019Article
- From Neurons to Social Beings: Short Review of the Mirror Neuron System Research and Its Socio-Psychological and Psychiatric Implications.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2018Review
- Computational Modeling of Gene-Specific Transcriptional Repression, Activation and Chromatin Interactions in Leukemogenesis by LASSO-Regularized Logistic Regression.IEEE/ACM transactions on computational biology and bioinformaticsArticle
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe aim of this study was to identify a transcriptomic signature that could be used to classify subjects with autism spectrum disorder (ASD) compared to controls on the basis of blood gene expression profiles. The gene expression profiles could ultimately be used as diagnostic biomarkers for ASD.
methodsWe used the published microarray data (GSE26415) from the Gene Expression Omnibus database, which included 21 young adults with ASD and 21 age- and sex-matched unaffected controls. Nineteen differentially expressed probes were identified from a training dataset (n=26, 13 ASD cases and 13 controls) using the limma package in R language (adjusted
resultsHierarchical cluster analysis showed that subjects with ASD were relatively well-discriminated from controls. Based on the support vector machine and K-nearest neighbors analysis, validation of 19-DE probes with a test dataset resulted in an overall class prediction accuracy of 93.8% as well as a sensitivity and specificity of 100% and 87.5%, respectively.
conclusionThe results of our exploratory study suggest that the gene expression profiles identified from the peripheral blood samples of young adults with ASD can be used to identify a biological signature for ASD. Further study using a larger cohort and more homogeneous datasets is required to improve the diagnostic accuracy.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.