OPADE Project: AI-driven Multi-omics for Predictive Antidepressant Efficacy

This project aims to develop an AI/ML-powered predictive tool to optimize antidepressant efficacy for patients with Major Depressive Disorders (MDD).The core task is the computational workload of Work Package 4 (WP4), which involves training and validating this AI tool. This requires processing massive, heterogeneous datasets from 350 patients over 24 months, including multi-omics (microbiome, metabolome, proteome, epigenome), clinical data, and real-time EEG brainwave data. This "unmanageable" volume of data requires automated bioinformatics pipelines and advanced AI/ML models (AutoML, 1-D CNNs, XAI) that are computationally intensive and exceed standard cloud capabilities.