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.
Digital Health
|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.
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.
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