Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk (sequence model release)

This is the deep learning sequence model used in Jian Zhou, Chandra L. Theesfeld, Kevin Yao, Kathleen M. Chen, Aaron K. Wong, and Olga G. Troyanskaya, Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk, Nature Genetics, 2018. Note the full software is...

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Bibliographic Details
Main Author: Zhou, Jian
Format: Dataset
Language:unknown
Published: Zenodo 2018
Subjects:
Beluga
Beluga*
Online Access:https://dx.doi.org/10.5281/zenodo.1744799
https://zenodo.org/record/1744799
id ftdatacite:10.5281/zenodo.1744799
record_format openpolar
spelling ftdatacite:10.5281/zenodo.1744799 2023-05-15T15:41:50+02:00 Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk (sequence model release) Zhou, Jian 2018 https://dx.doi.org/10.5281/zenodo.1744799 https://zenodo.org/record/1744799 unknown Zenodo https://dx.doi.org/10.5281/zenodo.1744798 Open Access info:eu-repo/semantics/openAccess dataset Dataset 2018 ftdatacite https://doi.org/10.5281/zenodo.1744799 https://doi.org/10.5281/zenodo.1744798 2021-11-05T12:55:41Z This is the deep learning sequence model used in Jian Zhou, Chandra L. Theesfeld, Kevin Yao, Kathleen M. Chen, Aaron K. Wong, and Olga G. Troyanskaya, Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk, Nature Genetics, 2018. Note the full software is available from https://github.com/FunctionLab/ExPecto and this release is created for the convenience of use and under the same non-commercial license. The model is in serialized torch t7 format that can be loaded in pytorch too with load_lua function. We also provide a web server for browsing mutations with strong predicted effects at https://hb.flatironinstitute.org/expecto/, which are currently limited to mutations within 1kb to TSS or are 1000 Genomes variants. Trivia: we code-named our models with whale names. This model has an unofficial codename DeepSEA "Beluga". Dataset Beluga Beluga* DataCite Metadata Store (German National Library of Science and Technology)
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language unknown
description This is the deep learning sequence model used in Jian Zhou, Chandra L. Theesfeld, Kevin Yao, Kathleen M. Chen, Aaron K. Wong, and Olga G. Troyanskaya, Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk, Nature Genetics, 2018. Note the full software is available from https://github.com/FunctionLab/ExPecto and this release is created for the convenience of use and under the same non-commercial license. The model is in serialized torch t7 format that can be loaded in pytorch too with load_lua function. We also provide a web server for browsing mutations with strong predicted effects at https://hb.flatironinstitute.org/expecto/, which are currently limited to mutations within 1kb to TSS or are 1000 Genomes variants. Trivia: we code-named our models with whale names. This model has an unofficial codename DeepSEA "Beluga".
format Dataset
author Zhou, Jian
spellingShingle Zhou, Jian
Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk (sequence model release)
author_facet Zhou, Jian
author_sort Zhou, Jian
title Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk (sequence model release)
title_short Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk (sequence model release)
title_full Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk (sequence model release)
title_fullStr Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk (sequence model release)
title_full_unstemmed Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk (sequence model release)
title_sort deep learning sequence-based ab initio prediction of variant effects on expression and disease risk (sequence model release)
publisher Zenodo
publishDate 2018
url https://dx.doi.org/10.5281/zenodo.1744799
https://zenodo.org/record/1744799
genre Beluga
Beluga*
genre_facet Beluga
Beluga*
op_relation https://dx.doi.org/10.5281/zenodo.1744798
op_rights Open Access
info:eu-repo/semantics/openAccess
op_doi https://doi.org/10.5281/zenodo.1744799
https://doi.org/10.5281/zenodo.1744798
_version_ 1766374716895920128

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