From ATR-FTIR Spectra to Visibility Graphs: An End-to-End GNN Pipeline for ASD Detection - Araújo, Lucas G. T.; Sabino-Silva, Robinson; Carneiro, Murillo G. (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS), 2026)
Autism Spectrum Disorder (ASD) lacks objective biomarkers, with diagnosis relying on behavioural observation and taking years. Salivary ATR-FTIR spectroscopy offers a non-invasive molecular fingerprint, but prior graph-based methods depend on hand-crafted topological features. We propose an end-to-end GNN pipeline that encodes each spectrum as a windowed visibility graph with a five-dimensional node feature vector and evaluates five architectures under stratified group cross-validation. GCN achieves F1MH = 0.810 in cross-validation and GIN 0.71 on the held-out test, competitive with prior graph-based approaches without hand-crafted features, establishing end-to-end GNN classification of ATR-FTIR spectra as viable for ASD detection.