Network-Based Detection of Autism Spectrum Disorder Using Sustainable and Non-invasive Salivary Biomarkers
This provides a potential non-invasive diagnostic tool for ASD, but it is incremental as it builds on existing biomarker and network analysis methods.
The paper tackled the problem of early diagnosis of Autism Spectrum Disorder (ASD) by developing GANet, a network optimization framework using salivary biomarkers, which achieved 0.78 accuracy, 0.61 sensitivity, and 0.90 specificity.
Autism Spectrum Disorder (ASD) lacks reliable biological markers, delaying early diagnosis. Using 159 salivary samples analyzed by ATR-FTIR spectroscopy, we developed GANet, a genetic algorithm-based network optimization framework leveraging PageRank and Degree for importance-based feature characterization. GANet systematically optimizes network structure to extract meaningful patterns from high-dimensional spectral data. It achieved superior performance compared to linear discriminant analysis, support vector machines, and deep learning models, reaching 0.78 accuracy, 0.61 sensitivity, 0.90 specificity, and a 0.74 harmonic mean. These results demonstrate GANet's potential as a robust, bio-inspired, non-invasive tool for precise ASD detection and broader spectral-based health applications.