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A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network

BACKGROUND: Drug-target interaction prediction is of great significance for narrowing down the scope of candidate medications, and thus is a vital step in drug discovery. Because of the particularity of biochemical experiments, the development of new drugs is not only costly, but also time-consuming...

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Detalles Bibliográficos
Autores principales: Peng, Jiajie, Li, Jingyi, Shang, Xuequn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7495825/
https://www.ncbi.nlm.nih.gov/pubmed/32938374
http://dx.doi.org/10.1186/s12859-020-03677-1
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author Peng, Jiajie
Li, Jingyi
Shang, Xuequn
author_facet Peng, Jiajie
Li, Jingyi
Shang, Xuequn
author_sort Peng, Jiajie
collection PubMed
description BACKGROUND: Drug-target interaction prediction is of great significance for narrowing down the scope of candidate medications, and thus is a vital step in drug discovery. Because of the particularity of biochemical experiments, the development of new drugs is not only costly, but also time-consuming. Therefore, the computational prediction of drug target interactions has become an essential way in the process of drug discovery, aiming to greatly reducing the experimental cost and time. RESULTS: We propose a learning-based method based on feature representation learning and deep neural network named DTI-CNN to predict the drug-target interactions. We first extract the relevant features of drugs and proteins from heterogeneous networks by using the Jaccard similarity coefficient and restart random walk model. Then, we adopt a denoising autoencoder model to reduce the dimension and identify the essential features. Third, based on the features obtained from last step, we constructed a convolutional neural network model to predict the interaction between drugs and proteins. The evaluation results show that the average AUROC score and AUPR score of DTI-CNN were 0.9416 and 0.9499, which obtains better performance than the other three existing state-of-the-art methods. CONCLUSIONS: All the experimental results show that the performance of DTI-CNN is better than that of the three existing methods and the proposed method is appropriately designed.
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spelling pubmed-74958252020-09-23 A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network Peng, Jiajie Li, Jingyi Shang, Xuequn BMC Bioinformatics Research BACKGROUND: Drug-target interaction prediction is of great significance for narrowing down the scope of candidate medications, and thus is a vital step in drug discovery. Because of the particularity of biochemical experiments, the development of new drugs is not only costly, but also time-consuming. Therefore, the computational prediction of drug target interactions has become an essential way in the process of drug discovery, aiming to greatly reducing the experimental cost and time. RESULTS: We propose a learning-based method based on feature representation learning and deep neural network named DTI-CNN to predict the drug-target interactions. We first extract the relevant features of drugs and proteins from heterogeneous networks by using the Jaccard similarity coefficient and restart random walk model. Then, we adopt a denoising autoencoder model to reduce the dimension and identify the essential features. Third, based on the features obtained from last step, we constructed a convolutional neural network model to predict the interaction between drugs and proteins. The evaluation results show that the average AUROC score and AUPR score of DTI-CNN were 0.9416 and 0.9499, which obtains better performance than the other three existing state-of-the-art methods. CONCLUSIONS: All the experimental results show that the performance of DTI-CNN is better than that of the three existing methods and the proposed method is appropriately designed. BioMed Central 2020-09-17 /pmc/articles/PMC7495825/ /pubmed/32938374 http://dx.doi.org/10.1186/s12859-020-03677-1 Text en © The Author(s) 2020 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.
spellingShingle Research
Peng, Jiajie
Li, Jingyi
Shang, Xuequn
A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network
title A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network
title_full A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network
title_fullStr A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network
title_full_unstemmed A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network
title_short A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network
title_sort learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7495825/
https://www.ncbi.nlm.nih.gov/pubmed/32938374
http://dx.doi.org/10.1186/s12859-020-03677-1
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