  Clinical variant classification of pathogenic versus benign genetic variants
remains a pivotal challenge in clinical genetics. Recently, the proposition of
protein language models has improved the generic variant effect prediction
(VEP) accuracy via weakly-supervised or unsupervised training. However, these
VEPs are not disease-specific, limiting their adaptation at point-of-care. To
address this problem, we propose a disease-specific \textsc{pro}tein language
model for variant \textsc{path}ogenicity, termed ProPath, to capture the
pseudo-log-likelihood ratio in rare missense variants through a siamese
network. We evaluate the performance of ProPath against pre-trained language
models, using clinical variant sets in inherited cardiomyopathies and
arrhythmias that were not seen during training. Our results demonstrate that
ProPath surpasses the pre-trained ESM1b with an over $5\%$ improvement in AUC
across both datasets. Furthermore, our model achieved the highest performances
across all baselines for both datasets. Thus, our ProPath offers a potent
disease-specific variant effect prediction, particularly valuable for disease
associations and clinical applicability.
