[논문]Prediction of mutation effects using a deep temporal convolutional network

Kim, Ha Young; Kim, Dongsup

doi:10.1093/bioinformatics/btz873

[해외논문] Prediction of mutation effects using a deep temporal convolutional network

Bioinformatics, v.36 no.7, 2020년, pp.2047 - 2052

Kim, Ha Young (Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology , Daejeon 34141, Republic of Korea) , Kim, Dongsup (Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology , Daejeon 34141, Republic of Korea)

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AbstractMotivationAccurate prediction of the effects of genetic variation is a major goal in biological research. Towards this goal, numerous machine learning models have been developed to learn information from evolutionary sequence data. The most effective method so far is a deep generative model based on the variational autoencoder (VAE) that models the distributions using a latent variable. In this study, we propose a deep autoregressive generative model named mutationTCN, which employs dilated causal convolutions and attention mechanism for the modeling of inter-residue correlations in a biological sequence.ResultsWe show that this model is competitive with the VAE model when tested against a set of 42 high-throughput mutation scan experiments, with the mean improvement in Spearman rank correlation ∼0.023. In particular, our model can more efficiently capture information from multiple sequence alignments with lower effective number of sequences, such as in viral sequence families, compared with the latent variable model. Also, we extend this architecture to a semi-supervised learning framework, which shows high prediction accuracy. We show that our model enables a direct optimization of the data likelihood and allows for a simple and stable training process.Availability and implementationSource code is available at https://github.com/ha01994/mutationTCN.Supplementary informationSupplementary data are available at Bioinformatics online.

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DOI : 10.1093/bioinformatics/btz873
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