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Artificial intelligence and bioinformatics analyze markers of children's transcriptional genome to predict autism
Huitao Tang1, Jiawei Liang2, Keping Chai1
1Department of Pediatrics, Zhejiang Hospital, Hangzhou, China.
Insights
This study identifies 21 immune-related biomarkers from toddler blood mRNA to aid early autism spectrum disorder (ASD) diagnosis. Machine learning models achieved 86% accuracy and 0.88 AUC, offering potential for timely intervention.
Area of Science:
- Genomics
- Immunology
- Computational Biology
Background:
- Autism spectrum disorder (ASD) diagnosis in toddlers is challenging.
- Early diagnosis and intervention are critical for managing ASD.
- Identifying reliable biomarkers for early detection is a key research area.
Purpose of the Study:
- To develop a machine learning model for early ASD diagnosis in toddlers using mRNA expression data.
- To identify key mRNA biomarkers associated with ASD in peripheral blood.
- To investigate the role of immune-related pathways in toddler ASD.
Main Methods:
- Collected peripheral blood mRNA expression data from 128 toddlers with ASD and 126 controls.
- Identified differentially expressed genes (DEGs) between ASD and control groups.
- Utilized LASSO regression to screen 21 key mRNA biomarkers and developed a neural network model.
Main Results:
- Identified DEGs including UBE4B, SPATA2, and RBM3, primarily linked to immune pathways.
- Screened 21 genes as key biomarkers with 86% classification accuracy.
- Achieved an Area Under the Curve (AUC) of 0.88 using a neural network model based on these biomarkers.
Conclusions:
- The identified immune-related biomarkers and mRNA expression profile show promise for facilitating early ASD diagnosis in toddlers.
- These findings contribute to understanding the biological underpinnings of ASD.
- Further validation with larger sample sizes is necessary to confirm the utility of these biomarkers.
Introduction:
Autism spectrum disorder (ASD), characterized by difficulties in social interaction and communication as well as restricted interests and repetitive behaviors, is extremely challenging to diagnose in toddlers. Early diagnosis and intervention are crucial however.
Methods:
In this study, we developed a machine learning classification model based on mRNA expression data from the peripheral blood of 128 toddlers with ASD and 126 controls. Differentially expressed genes (DEGs) between ASD and controls were identified.
Results:
We identified genes such as UBE4B, SPATA2 and RBM3 as DEGs, mainly involved in immune-related pathways. 21 genes were screened as key biomarkers using LASSO regression, yielding an accuracy of 86%. A neural network model based on these 21 genes achieved an AUC of 0.88.
Discussion:
Our findings suggest that the identified neurotransmitters and 21 immune-related biomarkers may facilitate the early diagnosis of ASD. The mRNA expression profile sheds light on the biological underpinnings of ASD in toddlers and potential biomarkers for early identification. Nevertheless, larger samples are needed to validate these biomarkers.
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