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Adverse Drug Reaction Predictions Using Stacking Deep Heterogeneous Information Network Embedding Approach
Inferring potential adverse drug reactions is an important and challenging task for the drug discovery and healthcare industry. Many previous studies in computational pharmacology have proposed utilizing multi-source drug information to predict drug side effects have and achieved initial success. Ho...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6320974/ https://www.ncbi.nlm.nih.gov/pubmed/30518099 http://dx.doi.org/10.3390/molecules23123193 |
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author | Hu, Baofang Wang, Hong Wang, Lutong Yuan, Weihua |
author_facet | Hu, Baofang Wang, Hong Wang, Lutong Yuan, Weihua |
author_sort | Hu, Baofang |
collection | PubMed |
description | Inferring potential adverse drug reactions is an important and challenging task for the drug discovery and healthcare industry. Many previous studies in computational pharmacology have proposed utilizing multi-source drug information to predict drug side effects have and achieved initial success. However, most of the prediction methods mainly rely on direct similarities inferred from drug information and cannot fully utilize the drug information about the impact of protein–protein interactions (PPI) on potential drug targets. Moreover, most of the methods are designed for specific tasks. In this work, we propose a novel heterogeneous network embedding approach for learning drug representations called SDHINE, which integrates PPI information into drug embeddings and is generic for different adverse drug reaction (ADR) prediction tasks. To integrate heterogeneous drug information and learn drug representations, we first design different meta-path-based proximities to calculate drug similarities, especially target propagation meta-path-based proximity based on PPI network, and then construct a semi-supervised stacking deep neural network model that is jointly optimized by the defined meta-path proximities. Extensive experiments with three state-of-the-art network embedding methods on three ADR prediction tasks demonstrate the effectiveness of the SDHINE model. Furthermore, we compare the drug representations in terms of drug differentiation by mapping the representations into 2D space; the results show that the performance of our approach is superior to that of the comparison methods. |
format | Online Article Text |
id | pubmed-6320974 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-63209742019-01-14 Adverse Drug Reaction Predictions Using Stacking Deep Heterogeneous Information Network Embedding Approach Hu, Baofang Wang, Hong Wang, Lutong Yuan, Weihua Molecules Article Inferring potential adverse drug reactions is an important and challenging task for the drug discovery and healthcare industry. Many previous studies in computational pharmacology have proposed utilizing multi-source drug information to predict drug side effects have and achieved initial success. However, most of the prediction methods mainly rely on direct similarities inferred from drug information and cannot fully utilize the drug information about the impact of protein–protein interactions (PPI) on potential drug targets. Moreover, most of the methods are designed for specific tasks. In this work, we propose a novel heterogeneous network embedding approach for learning drug representations called SDHINE, which integrates PPI information into drug embeddings and is generic for different adverse drug reaction (ADR) prediction tasks. To integrate heterogeneous drug information and learn drug representations, we first design different meta-path-based proximities to calculate drug similarities, especially target propagation meta-path-based proximity based on PPI network, and then construct a semi-supervised stacking deep neural network model that is jointly optimized by the defined meta-path proximities. Extensive experiments with three state-of-the-art network embedding methods on three ADR prediction tasks demonstrate the effectiveness of the SDHINE model. Furthermore, we compare the drug representations in terms of drug differentiation by mapping the representations into 2D space; the results show that the performance of our approach is superior to that of the comparison methods. MDPI 2018-12-04 /pmc/articles/PMC6320974/ /pubmed/30518099 http://dx.doi.org/10.3390/molecules23123193 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Hu, Baofang Wang, Hong Wang, Lutong Yuan, Weihua Adverse Drug Reaction Predictions Using Stacking Deep Heterogeneous Information Network Embedding Approach |
title | Adverse Drug Reaction Predictions Using Stacking Deep Heterogeneous Information Network Embedding Approach |
title_full | Adverse Drug Reaction Predictions Using Stacking Deep Heterogeneous Information Network Embedding Approach |
title_fullStr | Adverse Drug Reaction Predictions Using Stacking Deep Heterogeneous Information Network Embedding Approach |
title_full_unstemmed | Adverse Drug Reaction Predictions Using Stacking Deep Heterogeneous Information Network Embedding Approach |
title_short | Adverse Drug Reaction Predictions Using Stacking Deep Heterogeneous Information Network Embedding Approach |
title_sort | adverse drug reaction predictions using stacking deep heterogeneous information network embedding approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6320974/ https://www.ncbi.nlm.nih.gov/pubmed/30518099 http://dx.doi.org/10.3390/molecules23123193 |
work_keys_str_mv | AT hubaofang adversedrugreactionpredictionsusingstackingdeepheterogeneousinformationnetworkembeddingapproach AT wanghong adversedrugreactionpredictionsusingstackingdeepheterogeneousinformationnetworkembeddingapproach AT wanglutong adversedrugreactionpredictionsusingstackingdeepheterogeneousinformationnetworkembeddingapproach AT yuanweihua adversedrugreactionpredictionsusingstackingdeepheterogeneousinformationnetworkembeddingapproach |