biostudies-literatureKim HYNational Research Foundation of Korea19127https://www.ebi.ac.uk/biostudies/studies/S-EPMC8476491biostudies-literatureUnknown11(1)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 suScientific reportsAn enhanced variant effect predictor based on a deep generative model and the Born-Again Networks.PMC84764912017M3A9C4065952Kim DKim HYJeon WfalseAn enhanced variant effect predictor based on a deep generative model and the Born-Again Networks.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 su2021-01-01T00:00:00Z2021 Sep2025-04-03T22:44:02.939Z2022-02-11T11:29:57.351ZS-EPMC84764913458038310.1038/s41598-021-98693-3