Exploring Machine Learning for Early Autism Spectrum Disorder Prediction Using Umbilical Cord Blood Gene Expression - G. Speggiorin, Laura; W. Kowalski, Thayne; Recamonde-Mendoza, Mariana (Anais do Simpósio Brasileiro de Bioinformática (BSB), 2025)
This study presents a proof-of-concept machine learning (ML) model for early Autism Spectrum Disorder (ASD) prediction using transcriptomic data from umbilical cord blood. We analyzed 224 samples (53 ASD, 80 with non-typical development [Non-TD], 91 with typical development [TD]) from high-risk cohorts, proposing a two-step classification pipeline based on eight distinct algorithms and ensemble approaches. The first model (TD vs. Non-TD/ASD) achieved an F1.5-score of 0.89 and recall of 1.0; the second model (ASD vs. Non-TD) yielded 75% accuracy and an F1.5-score of 0.54. Results suggest subtle, yet detectable, transcriptomic signals in perinatal blood that may support early ASD risk stratification, warranting further investigation in larger cohorts.