CLinNET: An Interpretable and Uncertainty‐Aware Deep Learning Framework for Multi‐Modal Clinical Genomics

Abstract Identifying molecular drivers and diagnostic genes for neurocognitive disorders (NDs) remains a major challenge due to the prevalence of variants of uncertain significance (VUS) and limitations in current diagnostic platforms. While artificial intelligence (AI) offers potential solutions, existing models often lack interpretability and fail to address uncertainty, limiting clinical utility. CLinNET, a multi‐modal deep neural network with a dual‐branch design integrating sequencing data, gene expression, biological pathways, and gene ontology (GO) is introduced to enhance gene curation and VUS interpretation. CLinNET employs a biologically informed architecture, confidence‐based uncertainty quantification, and layer‐wise SHapley Additive exPlanations (SHAP) for robust interpretability. Its sparse networks, enriched with pathway and GO data, prioritize tissue‐expressed genes to improve prediction accuracy and biological relevance. Trained on ND datasets, CLinNET outperformed existing methods with an F1‐score of 76.4%, accuracy of 77.2%, and area under the precision‐recall curve (AUC‐PR) of 84%. Incorporating uncertainty filtering further improved precision to 87% while retaining 73% of predictions as high‐confidence. CLinNET identified significantly more ND‐associated genes than random permutations, with minimal overlap with cardiovascular‐associated genes, confirming specificity. Among the top decile of ranked genes, 78 were linked to NDs ( p ‐value = 1.2e‐11), and 372 to rare diseases involving nervous system abnormalities, highlighting their diagnostic potential. CLinNET's validation in prostate cancer datasets underscores its adaptability, positioning it as a robust tool for individualized medicine.