Cargando…
Inferring Drug-Related Diseases Based on Convolutional Neural Network and Gated Recurrent Unit
Predicting novel uses for drugs using their chemical, pharmacological, and indication information contributes to minimizing costs and development periods. Most previous prediction methods focused on integrating the similarity and association information of drugs and diseases. However, they tended to...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6696443/ https://www.ncbi.nlm.nih.gov/pubmed/31349692 http://dx.doi.org/10.3390/molecules24152712 |
_version_ | 1783444271569829888 |
---|---|
author | Xuan, Ping Zhao, Lianfeng Zhang, Tiangang Ye, Yilin Zhang, Yan |
author_facet | Xuan, Ping Zhao, Lianfeng Zhang, Tiangang Ye, Yilin Zhang, Yan |
author_sort | Xuan, Ping |
collection | PubMed |
description | Predicting novel uses for drugs using their chemical, pharmacological, and indication information contributes to minimizing costs and development periods. Most previous prediction methods focused on integrating the similarity and association information of drugs and diseases. However, they tended to construct shallow prediction models to predict drug-associated diseases, which make deeply integrating the information difficult. Further, path information between drugs and diseases is important auxiliary information for association prediction, while it is not deeply integrated. We present a deep learning-based method, CGARDP, for predicting drug-related candidate disease indications. CGARDP establishes a feature matrix by exploiting a variety of biological premises related to drugs and diseases. A novel model based on convolutional neural network (CNN) and gated recurrent unit (GRU) is constructed to learn the local and path representations for a drug-disease pair. The CNN-based framework on the left of the model learns the local representation of the drug-disease pair from their feature matrix. As the different paths have discriminative contributions to the drug-disease association prediction, we construct an attention mechanism at the path level to learn the informative paths. In the right part, a GRU-based framework learns the path representation based on path information between the drug and the disease. Cross-validation results indicate that CGARDP performs better than several state-of-the-art methods. Further, CGARDP retrieves more real drug-disease associations in the top part of the prediction result that are of concern to biologists. Case studies on five drugs demonstrate that CGARDP can discover potential drug-related disease indications. |
format | Online Article Text |
id | pubmed-6696443 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-66964432019-09-05 Inferring Drug-Related Diseases Based on Convolutional Neural Network and Gated Recurrent Unit Xuan, Ping Zhao, Lianfeng Zhang, Tiangang Ye, Yilin Zhang, Yan Molecules Article Predicting novel uses for drugs using their chemical, pharmacological, and indication information contributes to minimizing costs and development periods. Most previous prediction methods focused on integrating the similarity and association information of drugs and diseases. However, they tended to construct shallow prediction models to predict drug-associated diseases, which make deeply integrating the information difficult. Further, path information between drugs and diseases is important auxiliary information for association prediction, while it is not deeply integrated. We present a deep learning-based method, CGARDP, for predicting drug-related candidate disease indications. CGARDP establishes a feature matrix by exploiting a variety of biological premises related to drugs and diseases. A novel model based on convolutional neural network (CNN) and gated recurrent unit (GRU) is constructed to learn the local and path representations for a drug-disease pair. The CNN-based framework on the left of the model learns the local representation of the drug-disease pair from their feature matrix. As the different paths have discriminative contributions to the drug-disease association prediction, we construct an attention mechanism at the path level to learn the informative paths. In the right part, a GRU-based framework learns the path representation based on path information between the drug and the disease. Cross-validation results indicate that CGARDP performs better than several state-of-the-art methods. Further, CGARDP retrieves more real drug-disease associations in the top part of the prediction result that are of concern to biologists. Case studies on five drugs demonstrate that CGARDP can discover potential drug-related disease indications. MDPI 2019-07-25 /pmc/articles/PMC6696443/ /pubmed/31349692 http://dx.doi.org/10.3390/molecules24152712 Text en © 2019 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 Xuan, Ping Zhao, Lianfeng Zhang, Tiangang Ye, Yilin Zhang, Yan Inferring Drug-Related Diseases Based on Convolutional Neural Network and Gated Recurrent Unit |
title | Inferring Drug-Related Diseases Based on Convolutional Neural Network and Gated Recurrent Unit |
title_full | Inferring Drug-Related Diseases Based on Convolutional Neural Network and Gated Recurrent Unit |
title_fullStr | Inferring Drug-Related Diseases Based on Convolutional Neural Network and Gated Recurrent Unit |
title_full_unstemmed | Inferring Drug-Related Diseases Based on Convolutional Neural Network and Gated Recurrent Unit |
title_short | Inferring Drug-Related Diseases Based on Convolutional Neural Network and Gated Recurrent Unit |
title_sort | inferring drug-related diseases based on convolutional neural network and gated recurrent unit |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6696443/ https://www.ncbi.nlm.nih.gov/pubmed/31349692 http://dx.doi.org/10.3390/molecules24152712 |
work_keys_str_mv | AT xuanping inferringdrugrelateddiseasesbasedonconvolutionalneuralnetworkandgatedrecurrentunit AT zhaolianfeng inferringdrugrelateddiseasesbasedonconvolutionalneuralnetworkandgatedrecurrentunit AT zhangtiangang inferringdrugrelateddiseasesbasedonconvolutionalneuralnetworkandgatedrecurrentunit AT yeyilin inferringdrugrelateddiseasesbasedonconvolutionalneuralnetworkandgatedrecurrentunit AT zhangyan inferringdrugrelateddiseasesbasedonconvolutionalneuralnetworkandgatedrecurrentunit |