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

SUBMITTER: Bakhshayeshi I 

PROVIDER: S-EPMC12948272 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature