Genetic mutations can impact protein-protein interactions (PPIs) in biomedical literature. Automated extraction of PPIs affected by gene mutations from biomedical literature can aid in evaluating the clinical importance of gene variations, which is crucial for the advancement of precision medicine. In this study, a new model called the Gaussian-enhanced representation model (GRM) is introduced for PPI extraction. The model utilizes the Gaussian probability distribution to produce a target entity representation based on the BioBERT pretraining model. The GRM assigns more weight to target protein entities and their adjacent entities, resolving the problem of lengthy input text and scattered distribution of target entities in the PPI extraction task. Additionally, the model introduces a supervised contrast learning approach to enhance its effectiveness and robustness. Experiments on the BioCreative VI data set demonstrate that our proposed GRM model has achieved state-of-the-art performance.

中文翻译：

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通过高斯增强表示和对比学习提取突变影响的蛋白质-蛋白质相互作用。

基因突变会影响生物医学文献中的蛋白质-蛋白质相互作用 (PPI)。从生物医学文献中自动提取受基因突变影响的 PPI 可以帮助评估基因变异的临床重要性，这对于精准医学的进步至关重要。在本研究中，引入了一种称为高斯增强表示模型（GRM）的新模型用于 PPI 提取。该模型利用高斯概率分布生成基于 BioBERT 预训练模型的目标实体表示。GRM为目标蛋白质实体及其相邻实体分配更多的权重，解决了PPI提取任务中输入文本冗长和目标实体分布分散的问题。此外，该模型引入了监督对比学习方法来增强其有效性和鲁棒性。BioCreative VI 数据集上的实验表明，我们提出的 GRM 模型已经实现了最先进的性能。