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Old drug repositioning and new drug discovery through similarity learning from drug-target joint feature spaces
BACKGROUND: Detection of new drug-target interactions by computational algorithms is of crucial value to both old drug repositioning and new drug discovery. Existing machine-learning methods rely only on experimentally validated drug-target interactions (i.e., positive samples) for the predictions....
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6933655/ https://www.ncbi.nlm.nih.gov/pubmed/31881829 http://dx.doi.org/10.1186/s12859-019-3238-y |
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author | Zheng, Yi Peng, Hui Zhang, Xiaocai Zhao, Zhixun Gao, Xiaoying Li, Jinyan |
author_facet | Zheng, Yi Peng, Hui Zhang, Xiaocai Zhao, Zhixun Gao, Xiaoying Li, Jinyan |
author_sort | Zheng, Yi |
collection | PubMed |
description | BACKGROUND: Detection of new drug-target interactions by computational algorithms is of crucial value to both old drug repositioning and new drug discovery. Existing machine-learning methods rely only on experimentally validated drug-target interactions (i.e., positive samples) for the predictions. Their performance is severely impeded by the lack of reliable negative samples. RESULTS: We propose a method to construct highly-reliable negative samples for drug target prediction by a pairwise drug-target similarity measurement and OCSVM with a high-recall constraint. On one hand, we measure the pairwise similarity between every two drug-target interactions by combining the chemical similarity between their drugs and the Gene Ontology-based similarity between their targets. Then we calculate the accumulative similarity with all known drug-target interactions for each unobserved drug-target interaction. On the other hand, we obtain the signed distance from OCSVM learned from the known interactions with high recall (≥0.95) for each unobserved drug-target interaction. After normalizing all accumulative similarities and signed distances to the range [0,1], we compute the score for each unobserved drug-target interaction via averaging its accumulative similarity and signed distance. Unobserved interactions with lower scores are preferentially served as reliable negative samples for the classification algorithms. The performance of the proposed method is evaluated on the interaction data between 1094 drugs and 1556 target proteins. Extensive comparison experiments using four classical classifiers and one domain predictive method demonstrate the superior performance of the proposed method. A better decision boundary has been learned from the constructed reliable negative samples. CONCLUSIONS: Proper construction of highly-reliable negative samples can help the classification models learn a clear decision boundary which contributes to the performance improvement. |
format | Online Article Text |
id | pubmed-6933655 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-69336552019-12-30 Old drug repositioning and new drug discovery through similarity learning from drug-target joint feature spaces Zheng, Yi Peng, Hui Zhang, Xiaocai Zhao, Zhixun Gao, Xiaoying Li, Jinyan BMC Bioinformatics Research BACKGROUND: Detection of new drug-target interactions by computational algorithms is of crucial value to both old drug repositioning and new drug discovery. Existing machine-learning methods rely only on experimentally validated drug-target interactions (i.e., positive samples) for the predictions. Their performance is severely impeded by the lack of reliable negative samples. RESULTS: We propose a method to construct highly-reliable negative samples for drug target prediction by a pairwise drug-target similarity measurement and OCSVM with a high-recall constraint. On one hand, we measure the pairwise similarity between every two drug-target interactions by combining the chemical similarity between their drugs and the Gene Ontology-based similarity between their targets. Then we calculate the accumulative similarity with all known drug-target interactions for each unobserved drug-target interaction. On the other hand, we obtain the signed distance from OCSVM learned from the known interactions with high recall (≥0.95) for each unobserved drug-target interaction. After normalizing all accumulative similarities and signed distances to the range [0,1], we compute the score for each unobserved drug-target interaction via averaging its accumulative similarity and signed distance. Unobserved interactions with lower scores are preferentially served as reliable negative samples for the classification algorithms. The performance of the proposed method is evaluated on the interaction data between 1094 drugs and 1556 target proteins. Extensive comparison experiments using four classical classifiers and one domain predictive method demonstrate the superior performance of the proposed method. A better decision boundary has been learned from the constructed reliable negative samples. CONCLUSIONS: Proper construction of highly-reliable negative samples can help the classification models learn a clear decision boundary which contributes to the performance improvement. BioMed Central 2019-12-27 /pmc/articles/PMC6933655/ /pubmed/31881829 http://dx.doi.org/10.1186/s12859-019-3238-y Text en © The Author(s) 2019 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 Zheng, Yi Peng, Hui Zhang, Xiaocai Zhao, Zhixun Gao, Xiaoying Li, Jinyan Old drug repositioning and new drug discovery through similarity learning from drug-target joint feature spaces |
title | Old drug repositioning and new drug discovery through similarity learning from drug-target joint feature spaces |
title_full | Old drug repositioning and new drug discovery through similarity learning from drug-target joint feature spaces |
title_fullStr | Old drug repositioning and new drug discovery through similarity learning from drug-target joint feature spaces |
title_full_unstemmed | Old drug repositioning and new drug discovery through similarity learning from drug-target joint feature spaces |
title_short | Old drug repositioning and new drug discovery through similarity learning from drug-target joint feature spaces |
title_sort | old drug repositioning and new drug discovery through similarity learning from drug-target joint feature spaces |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6933655/ https://www.ncbi.nlm.nih.gov/pubmed/31881829 http://dx.doi.org/10.1186/s12859-019-3238-y |
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