An enhanced variant effect predictor based on a deep generative model and the Born-Again Networks

Abstract 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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ha Young Kim, Woosung Jeon, Dongsup Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0732861a9c9b4e0a9f75b5da1897d2f6
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:0732861a9c9b4e0a9f75b5da1897d2f6
record_format	dspace
spelling	oai:doaj.org-article:0732861a9c9b4e0a9f75b5da1897d2f62021-12-02T18:51:35ZAn enhanced variant effect predictor based on a deep generative model and the Born-Again Networks10.1038/s41598-021-98693-32045-2322https://doaj.org/article/0732861a9c9b4e0a9f75b5da1897d2f62021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98693-3https://doaj.org/toc/2045-2322Abstract 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.Ha Young KimWoosung JeonDongsup KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ha Young Kim Woosung Jeon Dongsup Kim An enhanced variant effect predictor based on a deep generative model and the Born-Again Networks
description	Abstract 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.
format	article
author	Ha Young Kim Woosung Jeon Dongsup Kim
author_facet	Ha Young Kim Woosung Jeon Dongsup Kim
author_sort	Ha Young Kim
title	An enhanced variant effect predictor based on a deep generative model and the Born-Again Networks
title_short	An enhanced variant effect predictor based on a deep generative model and the Born-Again Networks
title_full	An enhanced variant effect predictor based on a deep generative model and the Born-Again Networks
title_fullStr	An enhanced variant effect predictor based on a deep generative model and the Born-Again Networks
title_full_unstemmed	An enhanced variant effect predictor based on a deep generative model and the Born-Again Networks
title_sort	enhanced variant effect predictor based on a deep generative model and the born-again networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0732861a9c9b4e0a9f75b5da1897d2f6
work_keys_str_mv	AT hayoungkim anenhancedvarianteffectpredictorbasedonadeepgenerativemodelandthebornagainnetworks AT woosungjeon anenhancedvarianteffectpredictorbasedonadeepgenerativemodelandthebornagainnetworks AT dongsupkim anenhancedvarianteffectpredictorbasedonadeepgenerativemodelandthebornagainnetworks AT hayoungkim enhancedvarianteffectpredictorbasedonadeepgenerativemodelandthebornagainnetworks AT woosungjeon enhancedvarianteffectpredictorbasedonadeepgenerativemodelandthebornagainnetworks AT dongsupkim enhancedvarianteffectpredictorbasedonadeepgenerativemodelandthebornagainnetworks
_version_	1718377368082448384

An enhanced variant effect predictor based on a deep generative model and the Born-Again Networks

Ejemplares similares