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Autism Spectrum Disorder

Autism Spectrum Disorder

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.
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Related Experiment Video

Updated: May 30, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

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eNSMBL-PASD: Spearheading early autism spectrum disorder detection through advanced genomic computational frameworks

Ayesha Karim¹, Nashwan Alromema², Sharaf J Malebary³

¹Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan.

|January 28, 2025

Summary

A new computational model, eNSMBL-PASD, accurately predicts autism spectrum disorder (ASD) driver genes using genomic data. This tool aids in early ASD detection and intervention planning.

Keywords:

Autism spectrum disorder Random Forest autism detection ensemble modeling machine learning general

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Area of Science:

Computational biology
Genetics
Neurodevelopmental disorders

Background:

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with no definitive early diagnostic tests.
Early diagnosis and intervention are crucial for improving outcomes in ASD.
Identifying genetic factors is key to understanding and diagnosing ASD.

Purpose of the Study:

To develop a computational model for predicting autism spectrum disorder (ASD) driver genes.
To utilize genomic data for early ASD detection.
To enhance early intervention strategies for ASD.

Main Methods:

A benchmark genomic dataset was processed using feature extraction.
Ensemble classification methods (e.g., Extreme Gradient Boosting, Random Forest) were employed.
The Ensemble Model Predictor for Autism Spectrum Disorder (eNSMBL-PASD) was developed and validated.

Main Results:

The eNSMBL-PASD model achieved 100% accuracy in self-consistency tests.
Independent set and cross-validation tests showed 91% and 87% accuracy, respectively.
The model demonstrated robust and reliable prediction of ASD-related genes.

Conclusions:

The eNSMBL-PASD model shows promise for early ASD detection through genetic marker identification.
This tool can assist healthcare professionals in diagnosing and planning treatments for ASD.
Further development could significantly impact early intervention for individuals with ASD.