2604.00652 Benchmarking Classical Machine Learning and Neural Methods for Variant Pathogenicity Prediction on ClinVar Metadata
Predicting whether a genomic variant is pathogenic or benign is a central problem in clinical genomics. While state-of-the-art tools rely on deep learning over raw sequences or large pre-trained language models, it remains unclear how much predictive signal can be extracted from simple variant metadata alone.