Tuesday, Jun 25: 12:00 PM - 1:15 PM
3917
Oral Sessions
COEX
Room: ASEM Ballroom 202
Explainable machine learning of complex multimodal data in neuroscience research is revolutionizing precision neuropsychiatry. Interpretable clustering of patients into distinct subtypes can enhance personalized prognosis, diagnosis, and treatment. However, training on biomedical data poses challenges due to high dimensionality, clustering, and limited sample size. To address this, we propose a scalable approach for cluster-aware embedding, incorporating a convex clustering penalty. This approach facilitates hierarchical clustering of principal component analysis (PCA), locally linear embedding (LLE), and canonical correlation analysis (CCA). Our method improves upon existing techniques and offers a modular framework for interpretable biomarker discovery in precision medicine. We apply this approach to identify neurocognitive subtypes in the Adolescent Brain Cognitive Development (ABCD) and Autism Brain Imaging Data Exchange (ABIDE) datasets.