用唾液光谱数据和智能网络分析，实现自闭症非侵入式精准检测。
Network-Based Detection of Autism Spectrum Disorder Using Sustainable and Non-invasive Salivary Biomarkers
- 基于遗传算法优化网络结构，用页码排序与节点度筛选关键光谱特征。
- 在159份样本上达到0.78准确率、0.90特异性和0.74调和平均值。
- 适合关注无创生物标志物与复杂数据建模的研究者或临床筛查应用。
自闭症谱系障碍（ASD）缺乏可靠的生物标志物，导致早期诊断延迟。本研究利用159份唾液样本，通过ATR-FTIR光谱技术分析，提出GANet——一种基于遗传算法的网络优化框架，结合PageRank与节点度进行重要性驱动的特征表征。该方法系统优化网络结构，从高维光谱数据中提取有意义模式。相较于线性判别分析、支持向量机及深度学习模型，GANet表现更优：准确率达0.78，灵敏度为0.61，特异性达0.90，调和平均值为0.74。结果表明，GANet具备鲁棒性与生物启发性，是一种有前景的非侵入式工具，可用于精确识别自闭症，并拓展至其他光谱驱动的健康检测场景。
原文摘要 · Abstract (English)
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.
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