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.

我们提出了一种疾病特异性的蛋白质语言模型 ProPath，通过孪生网络捕捉罕见错义变异的伪对数似然比，从而解决了临床基因变异的分类问题，并在家族性心肌病和心律失常的临床变异集中取得了最好的性能表现。