A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information

Network-based data integration for drug–target prediction is a promising avenue for drug repositioning, but performance is wanting. Here, the authors introduce DTINet, whose performance is enhanced in the face of noisy, incomplete and high-dimensional biological data by learning low-dimensional vect...

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Autores principales: Yunan Luo, Xinbin Zhao, Jingtian Zhou, Jinglin Yang, Yanqing Zhang, Wenhua Kuang, Jian Peng, Ligong Chen, Jianyang Zeng
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2017
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Acceso en línea:https://doaj.org/article/af0ec87cc2674025b7de4031ef5792be
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spelling oai:doaj.org-article:af0ec87cc2674025b7de4031ef5792be2021-12-02T14:40:26ZA network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information10.1038/s41467-017-00680-82041-1723https://doaj.org/article/af0ec87cc2674025b7de4031ef5792be2017-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-017-00680-8https://doaj.org/toc/2041-1723Network-based data integration for drug–target prediction is a promising avenue for drug repositioning, but performance is wanting. Here, the authors introduce DTINet, whose performance is enhanced in the face of noisy, incomplete and high-dimensional biological data by learning low-dimensional vector representations.Yunan LuoXinbin ZhaoJingtian ZhouJinglin YangYanqing ZhangWenhua KuangJian PengLigong ChenJianyang ZengNature PortfolioarticleScienceQENNature Communications, Vol 8, Iss 1, Pp 1-13 (2017)
institution DOAJ
collection DOAJ
language EN
topic Science
Q
spellingShingle Science
Q
Yunan Luo
Xinbin Zhao
Jingtian Zhou
Jinglin Yang
Yanqing Zhang
Wenhua Kuang
Jian Peng
Ligong Chen
Jianyang Zeng
A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information
description Network-based data integration for drug–target prediction is a promising avenue for drug repositioning, but performance is wanting. Here, the authors introduce DTINet, whose performance is enhanced in the face of noisy, incomplete and high-dimensional biological data by learning low-dimensional vector representations.
format article
author Yunan Luo
Xinbin Zhao
Jingtian Zhou
Jinglin Yang
Yanqing Zhang
Wenhua Kuang
Jian Peng
Ligong Chen
Jianyang Zeng
author_facet Yunan Luo
Xinbin Zhao
Jingtian Zhou
Jinglin Yang
Yanqing Zhang
Wenhua Kuang
Jian Peng
Ligong Chen
Jianyang Zeng
author_sort Yunan Luo
title A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information
title_short A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information
title_full A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information
title_fullStr A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information
title_full_unstemmed A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information
title_sort network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information
publisher Nature Portfolio
publishDate 2017
url https://doaj.org/article/af0ec87cc2674025b7de4031ef5792be
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