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Predicting Drug-Disease Associations via Using Gaussian Interaction Profile and Kernel-Based Autoencoder
Computational drug repositioning, designed to identify new indications for existing drugs, significantly reduced the cost and time involved in drug development. Prediction of drug-disease associations is promising for drug repositioning. Recent years have witnessed an increasing number of machine le...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6732622/ https://www.ncbi.nlm.nih.gov/pubmed/31534955 http://dx.doi.org/10.1155/2019/2426958 |
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author | Jiang, Han-Jing Huang, Yu-An You, Zhu-Hong |
author_facet | Jiang, Han-Jing Huang, Yu-An You, Zhu-Hong |
author_sort | Jiang, Han-Jing |
collection | PubMed |
description | Computational drug repositioning, designed to identify new indications for existing drugs, significantly reduced the cost and time involved in drug development. Prediction of drug-disease associations is promising for drug repositioning. Recent years have witnessed an increasing number of machine learning-based methods for calculating drug repositioning. In this paper, a novel feature learning method based on Gaussian interaction profile kernel and autoencoder (GIPAE) is proposed for drug-disease association. In order to further reduce the computation cost, both batch normalization layer and the full-connected layer are introduced to reduce training complexity. The experimental results of 10-fold cross validation indicate that the proposed method achieves superior performance on Fdataset and Cdataset with the AUCs of 93.30% and 96.03%, respectively, which were higher than many previous computational models. To further assess the accuracy of GIPAE, we conducted case studies on two complex human diseases. The top 20 drugs predicted, 14 obesity-related drugs, and 11 drugs related to Alzheimer's disease were validated in the CTD database. The results of cross validation and case studies indicated that GIPAE is a reliable model for predicting drug-disease associations. |
format | Online Article Text |
id | pubmed-6732622 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-67326222019-09-18 Predicting Drug-Disease Associations via Using Gaussian Interaction Profile and Kernel-Based Autoencoder Jiang, Han-Jing Huang, Yu-An You, Zhu-Hong Biomed Res Int Research Article Computational drug repositioning, designed to identify new indications for existing drugs, significantly reduced the cost and time involved in drug development. Prediction of drug-disease associations is promising for drug repositioning. Recent years have witnessed an increasing number of machine learning-based methods for calculating drug repositioning. In this paper, a novel feature learning method based on Gaussian interaction profile kernel and autoencoder (GIPAE) is proposed for drug-disease association. In order to further reduce the computation cost, both batch normalization layer and the full-connected layer are introduced to reduce training complexity. The experimental results of 10-fold cross validation indicate that the proposed method achieves superior performance on Fdataset and Cdataset with the AUCs of 93.30% and 96.03%, respectively, which were higher than many previous computational models. To further assess the accuracy of GIPAE, we conducted case studies on two complex human diseases. The top 20 drugs predicted, 14 obesity-related drugs, and 11 drugs related to Alzheimer's disease were validated in the CTD database. The results of cross validation and case studies indicated that GIPAE is a reliable model for predicting drug-disease associations. Hindawi 2019-08-27 /pmc/articles/PMC6732622/ /pubmed/31534955 http://dx.doi.org/10.1155/2019/2426958 Text en Copyright © 2019 Han-Jing Jiang et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Jiang, Han-Jing Huang, Yu-An You, Zhu-Hong Predicting Drug-Disease Associations via Using Gaussian Interaction Profile and Kernel-Based Autoencoder |
title | Predicting Drug-Disease Associations via Using Gaussian Interaction Profile and Kernel-Based Autoencoder |
title_full | Predicting Drug-Disease Associations via Using Gaussian Interaction Profile and Kernel-Based Autoencoder |
title_fullStr | Predicting Drug-Disease Associations via Using Gaussian Interaction Profile and Kernel-Based Autoencoder |
title_full_unstemmed | Predicting Drug-Disease Associations via Using Gaussian Interaction Profile and Kernel-Based Autoencoder |
title_short | Predicting Drug-Disease Associations via Using Gaussian Interaction Profile and Kernel-Based Autoencoder |
title_sort | predicting drug-disease associations via using gaussian interaction profile and kernel-based autoencoder |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6732622/ https://www.ncbi.nlm.nih.gov/pubmed/31534955 http://dx.doi.org/10.1155/2019/2426958 |
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