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Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity
Drug-Drug Interaction (DDI) prediction is one of the most critical issues in drug development and health. Proposing appropriate computational methods for predicting unknown DDI with high precision is challenging. We proposed "NDD: Neural network-based method for drug-drug interaction prediction...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6754439/ https://www.ncbi.nlm.nih.gov/pubmed/31541145 http://dx.doi.org/10.1038/s41598-019-50121-3 |
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author | Rohani, Narjes Eslahchi, Changiz |
author_facet | Rohani, Narjes Eslahchi, Changiz |
author_sort | Rohani, Narjes |
collection | PubMed |
description | Drug-Drug Interaction (DDI) prediction is one of the most critical issues in drug development and health. Proposing appropriate computational methods for predicting unknown DDI with high precision is challenging. We proposed "NDD: Neural network-based method for drug-drug interaction prediction" for predicting unknown DDIs using various information about drugs. Multiple drug similarities based on drug substructure, target, side effect, off-label side effect, pathway, transporter, and indication data are calculated. At first, NDD uses a heuristic similarity selection process and then integrates the selected similarities with a nonlinear similarity fusion method to achieve high-level features. Afterward, it uses a neural network for interaction prediction. The similarity selection and similarity integration parts of NDD have been proposed in previous studies of other problems. Our novelty is to combine these parts with new neural network architecture and apply these approaches in the context of DDI prediction. We compared NDD with six machine learning classifiers and six state-of-the-art graph-based methods on three benchmark datasets. NDD achieved superior performance in cross-validation with AUPR ranging from 0.830 to 0.947, AUC from 0.954 to 0.994 and F-measure from 0.772 to 0.902. Moreover, cumulative evidence in case studies on numerous drug pairs, further confirm the ability of NDD to predict unknown DDIs. The evaluations corroborate that NDD is an efficient method for predicting unknown DDIs. The data and implementation of NDD are available at https://github.com/nrohani/NDD. |
format | Online Article Text |
id | pubmed-6754439 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-67544392019-10-02 Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity Rohani, Narjes Eslahchi, Changiz Sci Rep Article Drug-Drug Interaction (DDI) prediction is one of the most critical issues in drug development and health. Proposing appropriate computational methods for predicting unknown DDI with high precision is challenging. We proposed "NDD: Neural network-based method for drug-drug interaction prediction" for predicting unknown DDIs using various information about drugs. Multiple drug similarities based on drug substructure, target, side effect, off-label side effect, pathway, transporter, and indication data are calculated. At first, NDD uses a heuristic similarity selection process and then integrates the selected similarities with a nonlinear similarity fusion method to achieve high-level features. Afterward, it uses a neural network for interaction prediction. The similarity selection and similarity integration parts of NDD have been proposed in previous studies of other problems. Our novelty is to combine these parts with new neural network architecture and apply these approaches in the context of DDI prediction. We compared NDD with six machine learning classifiers and six state-of-the-art graph-based methods on three benchmark datasets. NDD achieved superior performance in cross-validation with AUPR ranging from 0.830 to 0.947, AUC from 0.954 to 0.994 and F-measure from 0.772 to 0.902. Moreover, cumulative evidence in case studies on numerous drug pairs, further confirm the ability of NDD to predict unknown DDIs. The evaluations corroborate that NDD is an efficient method for predicting unknown DDIs. The data and implementation of NDD are available at https://github.com/nrohani/NDD. Nature Publishing Group UK 2019-09-20 /pmc/articles/PMC6754439/ /pubmed/31541145 http://dx.doi.org/10.1038/s41598-019-50121-3 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Rohani, Narjes Eslahchi, Changiz Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity |
title | Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity |
title_full | Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity |
title_fullStr | Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity |
title_full_unstemmed | Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity |
title_short | Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity |
title_sort | drug-drug interaction predicting by neural network using integrated similarity |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6754439/ https://www.ncbi.nlm.nih.gov/pubmed/31541145 http://dx.doi.org/10.1038/s41598-019-50121-3 |
work_keys_str_mv | AT rohaninarjes drugdruginteractionpredictingbyneuralnetworkusingintegratedsimilarity AT eslahchichangiz drugdruginteractionpredictingbyneuralnetworkusingintegratedsimilarity |