ProPath：用于变异致病性分析的疾病特异性蛋白质语言模型
ProPath: Disease-Specific Protein Language Model for Variant Pathogenicity
AI总结：
针对现有通用变异效应预测模型缺乏疾病特异性、临床适配性不足的问题，本研究提出疾病特异性蛋白质语言模型ProPath，通过孪生网络捕捉罕见错义变异的伪对数似然比，在心肌病和心律失常数据集上的AUC较ESM1b提升超5%，为疾病特异性变异效应预测提供了有效工具。
摘要：
在临床遗传学中，对致病变异与良性基因变异进行临床变异分类仍是一项核心挑战。近期，蛋白质语言模型的提出通过弱监督或无监督训练，提升了通用变异效应预测（VEP）的准确率。但这类VEP不具备疾病特异性，限制了其在临床诊疗场景中的适配性。为解决这一问题，我们提出一种用于变异致病性分析的疾病特异性蛋白质语言模型，命名为ProPath，该模型通过孪生网络捕捉罕见错义变异中的伪对数似然比。我们使用训练阶段未见过的遗传性心肌病和心律失常临床变异集，将ProPath与预训练语言模型进行性能对比。结果显示，在两个数据集上，ProPath的AUC均比预训练ESM1b高出5%以上。此外，我们的模型在两个数据集的所有基线方法中均取得了最高性能。因此，ProPath提供了强有力的疾病特异性变异效应预测能力，对疾病关联研究和临床应用具有特别重要的价值。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
