DeNovoSeer:a deep learning framework for pathogenicity prediction of De novo mutations

De novo mutations (DNMs) play a crucial role in the pathogenesis and clinical interpretation of genetic diseases. However, existing pathogenicity prediction methods either uniformly handle all variants or focus on specific variant types, lacking a systematic prediction framework for DNMs and failing to effectively integrate functional annotation with clinical evidence. In this study, we present DeNovoSeer, a deep learning pathogenicity prediction framework for coding-region DNMs. Built upon a labeling system that combines high-confidence ClinVar annotations with complementary phenotype information from Gene4Denovo, the method integrates multi-source functional annotations and clinical evidence derived from the ACMG/AMP guidelines. It employs a semi-supervised convolutional-dilated convolution hybrid network architecture, enabling joint representation learning across multiple tasks under limited labeled data. On the Gene4Denovo test set, DeNovoSeer demonstrated stable performance across 10 independent random splits, achieving an AUC of 0.876 ± 0.007 and an AP of 0.881 ± 0.008, while outperforming existing tools. SHAP-based analysis provides feature-level attribution of model predictions, revealing biologically and clinically meaningful evidence patterns and offering interpretable support for variant assessment. This study proposes a systematic framework for pathogenicity prediction of coding-region DNMs, integrating robust label construction, semi-supervised representation learning, and clinical evidence integration. It provides new methodological support for molecular diagnosis and the interpretation of disease mechanisms.