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Deep learning-based transcriptome data classification for drug-target interaction prediction
BACKGROUND: The ability to predict the interaction of drugs with target proteins is essential to research and development of drug. However, the traditional experimental paradigm is costly, and previous in silico prediction paradigms have been impeded by the wide range of data platforms and data scar...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6156897/ https://www.ncbi.nlm.nih.gov/pubmed/30255785 http://dx.doi.org/10.1186/s12864-018-5031-0 |
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author | Xie, Lingwei He, Song Song, Xinyu Bo, Xiaochen Zhang, Zhongnan |
author_facet | Xie, Lingwei He, Song Song, Xinyu Bo, Xiaochen Zhang, Zhongnan |
author_sort | Xie, Lingwei |
collection | PubMed |
description | BACKGROUND: The ability to predict the interaction of drugs with target proteins is essential to research and development of drug. However, the traditional experimental paradigm is costly, and previous in silico prediction paradigms have been impeded by the wide range of data platforms and data scarcity. RESULTS: In this paper, we modeled the prediction of drug-target interactions as a binary classification task. Using transcriptome data from the L1000 database of the LINCS project, we developed a framework based on a deep-learning algorithm to predict potential drug target interactions. Once fully trained, the model achieved over 98% training accuracy. The results of our research demonstrated that our framework could discover more reliable DTIs than found by other methods. This conclusion was validated further across platforms with a high percentage of overlapping interactions. CONCLUSIONS: Our model’s capacity of integrating transcriptome data from drugs and genes strongly suggests the strength of its potential for DTI prediction, thereby improving the drug discovery process. |
format | Online Article Text |
id | pubmed-6156897 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-61568972018-09-27 Deep learning-based transcriptome data classification for drug-target interaction prediction Xie, Lingwei He, Song Song, Xinyu Bo, Xiaochen Zhang, Zhongnan BMC Genomics Research BACKGROUND: The ability to predict the interaction of drugs with target proteins is essential to research and development of drug. However, the traditional experimental paradigm is costly, and previous in silico prediction paradigms have been impeded by the wide range of data platforms and data scarcity. RESULTS: In this paper, we modeled the prediction of drug-target interactions as a binary classification task. Using transcriptome data from the L1000 database of the LINCS project, we developed a framework based on a deep-learning algorithm to predict potential drug target interactions. Once fully trained, the model achieved over 98% training accuracy. The results of our research demonstrated that our framework could discover more reliable DTIs than found by other methods. This conclusion was validated further across platforms with a high percentage of overlapping interactions. CONCLUSIONS: Our model’s capacity of integrating transcriptome data from drugs and genes strongly suggests the strength of its potential for DTI prediction, thereby improving the drug discovery process. BioMed Central 2018-09-24 /pmc/articles/PMC6156897/ /pubmed/30255785 http://dx.doi.org/10.1186/s12864-018-5031-0 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Xie, Lingwei He, Song Song, Xinyu Bo, Xiaochen Zhang, Zhongnan Deep learning-based transcriptome data classification for drug-target interaction prediction |
title | Deep learning-based transcriptome data classification for drug-target interaction prediction |
title_full | Deep learning-based transcriptome data classification for drug-target interaction prediction |
title_fullStr | Deep learning-based transcriptome data classification for drug-target interaction prediction |
title_full_unstemmed | Deep learning-based transcriptome data classification for drug-target interaction prediction |
title_short | Deep learning-based transcriptome data classification for drug-target interaction prediction |
title_sort | deep learning-based transcriptome data classification for drug-target interaction prediction |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6156897/ https://www.ncbi.nlm.nih.gov/pubmed/30255785 http://dx.doi.org/10.1186/s12864-018-5031-0 |
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