利用 DNA 序列对错义突变的致病性进行预测

错义突变的致病性预测在基因组学和临床研究中有重要作用，其中基于计算方法的预测工具已经取得较大进展。现有的工具大多根据功能影响、保守性来对错义突变的致病性进行预测，从 DNA 序列出发进行错义突变致病性预测的工具较少。随着自然语言处理技术在多个生物序列领域的迁移学习和应用，将 DNA 序列作为一种生物语言进行处理并进行基因突变的致病性预测越来越值得探索。该文提出了一个基于预训练的自然语言模型 DNABert 和 DNA 突变序列对错义突变致病性进行预测的深度学习模型MissenseBert，并且在多个数据集上对MissenseBert进行了训练和测试，测试结果说明MissenseBert取得了较好的预测效果，证明了利用 DNA 序列预测错义突变的致病性具有可行性。

Utilize DNA Sequences to Predict Pathogenicity of Missense Variants

Pathogenicity prediction of missense variants is important in genetic research and clinical application. Among current related methods, computational methods have been widely applied and shown outstanding performance. Most computational methods are based on functional impact of alteration or conservation of sequence. Considering some natural language processing (NLP) methods are used in biological sequence tasks by transfer learning, handling DNA sequences as a kind of biological language and predicting the pathogenicity of genetic variants are encouraging. Based on a pretrained NLP model and DNA sequences with altered allele, we propose a deep learning model named MissenseBert to predict the pathogenicity of missense variants. Training and evaluated with multiple datasets, MissenseBert achieves promising performance and illustrate the feasibility of predicting the pathogenicity by DNA sequence.