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An enhanced variant effect predictor based on a deep generative model and the Born-Again Networks
Scientific Reports ( IF 3.8 ) Pub Date : 2021-09-27 , DOI: 10.1038/s41598-021-98693-3
Ha Young Kim 1 , Woosung Jeon 1 , Dongsup Kim 1
Affiliation  

The development of an accurate and reliable variant effect prediction tool is important for research in human genetic diseases. A large number of predictors have been developed towards this goal, yet many of these predictors suffer from the problem of data circularity. Here we present MTBAN (Mutation effect predictor using the Temporal convolutional network and the Born-Again Networks), a method for predicting the deleteriousness of variants. We apply a form of knowledge distillation technique known as the Born-Again Networks (BAN) to a previously developed deep autoregressive generative model, mutationTCN, to achieve an improved performance in variant effect prediction. As the model is fully unsupervised and trained only on the evolutionarily related sequences of a protein, it does not suffer from the problem of data circularity which is common across supervised predictors. When evaluated on a test dataset consisting of deleterious and benign human protein variants, MTBAN shows an outstanding predictive ability compared to other well-known variant effect predictors. We also offer a user-friendly web server to predict variant effects using MTBAN, freely accessible at http://mtban.kaist.ac.kr. To our knowledge, MTBAN is the first variant effect prediction tool based on a deep generative model that provides a user-friendly web server for the prediction of deleteriousness of variants.



中文翻译:

基于深度生成模型和Born-Again Networks的增强型变异效应预测器

开发准确可靠的变异效应预测工具对于人类遗传疾病的研究非常重要。已经为此目标开发了大量预测器,但其中许多预测器都存在数据循环问题。在这里,我们提出了 MTBAN(使用时间卷积网络和 Born-Again 网络的突变效应预测器),这是一种预测变体有害性的方法。我们将一种称为重生网络 (BAN) 的知识蒸馏技术应用于先前开发的深度自回归生成模型mutationTCN,以提高变异效应预测的性能。由于该模型是完全无监督的,并且仅针对蛋白质的进化相关序列进行训练,它不会受到监督预测器中常见的数据循环问题的影响。当在由有害和良性人类蛋白质变体组成的测试数据集上进行评估时,MTBAN 与其他众所周知的变体效应预测器相比显示出出色的预测能力。我们还提供了一个用户友好的网络服务器来使用 MTBAN 预测变异效果,可在 http://mtban.kaist.ac.kr 免费访问。据我们所知,MTBAN 是第一个基于深度生成模型的变体效果预测工具,该模型为预测变体的有害性提供了一个用户友好的网络服务器。我们还提供了一个用户友好的网络服务器来使用 MTBAN 预测变异效果,可在 http://mtban.kaist.ac.kr 免费访问。据我们所知,MTBAN 是第一个基于深度生成模型的变体效果预测工具,该模型为预测变体的有害性提供了一个用户友好的网络服务器。我们还提供了一个用户友好的网络服务器来使用 MTBAN 预测变异效果,可在 http://mtban.kaist.ac.kr 免费访问。据我们所知,MTBAN 是第一个基于深度生成模型的变体效果预测工具,该模型为预测变体的有害性提供了一个用户友好的网络服务器。

更新日期:2021-09-27
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