A model for predicting drug-disease associations based on dense convolutional attention network

The development of new drugs is a time-consuming and labor-intensive process. Therefore, researchers use computational methods to explore other therapeutic effects of existing drugs, and drug-disease association prediction is an important branch of it. The existing drug-disease association predictio...

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Autores principales:	Huiqing Wang, Sen Zhao, Jing Zhao, Zhipeng Feng
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	drug-disease association prediction gaussian interaction profile kernel similarity dense convolutional neural network convolutional block attention module random forest classifier Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/029eeab8870949b59f8b66cf9f166132
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id	oai:doaj.org-article:029eeab8870949b59f8b66cf9f166132
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spelling	oai:doaj.org-article:029eeab8870949b59f8b66cf9f1661322021-11-23T02:10:15ZA model for predicting drug-disease associations based on dense convolutional attention network10.3934/mbe.20213671551-0018https://doaj.org/article/029eeab8870949b59f8b66cf9f1661322021-08-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021367?viewType=HTMLhttps://doaj.org/toc/1551-0018The development of new drugs is a time-consuming and labor-intensive process. Therefore, researchers use computational methods to explore other therapeutic effects of existing drugs, and drug-disease association prediction is an important branch of it. The existing drug-disease association prediction method ignored the prior knowledge contained in the drug-disease association data, which provided a strong basis for the research. Moreover, the previous methods only paid attention to the high-level features in the network when extracting features, and directly fused or connected them in series, resulting in the loss of information. Therefore, we propose a novel deep learning model for drug-disease association prediction, called DCNN. The model introduces the Gaussian interaction profile kernel similarity for drugs and diseases, and combines them with the structural similarity of drugs and the semantic similarity of diseases to construct the feature space jointly. Then dense convolutional neural network (DenseCNN) is used to capture the feature information of drugs and diseases, and introduces a convolutional block attention module (CBAM) to weight features from the channel and space levels to achieve adaptive optimization of features. The ten-fold cross-validation results of the model DCNN and the experimental results of the case study show that it is superior to the existing drug-disease association predictors and effectively predicts the drug-disease associations.Huiqing WangSen ZhaoJing ZhaoZhipeng FengAIMS Pressarticledrug-disease association predictiongaussian interaction profile kernel similaritydense convolutional neural networkconvolutional block attention modulerandom forest classifierBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 7419-7439 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	drug-disease association prediction gaussian interaction profile kernel similarity dense convolutional neural network convolutional block attention module random forest classifier Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	drug-disease association prediction gaussian interaction profile kernel similarity dense convolutional neural network convolutional block attention module random forest classifier Biotechnology TP248.13-248.65 Mathematics QA1-939 Huiqing Wang Sen Zhao Jing Zhao Zhipeng Feng A model for predicting drug-disease associations based on dense convolutional attention network
description	The development of new drugs is a time-consuming and labor-intensive process. Therefore, researchers use computational methods to explore other therapeutic effects of existing drugs, and drug-disease association prediction is an important branch of it. The existing drug-disease association prediction method ignored the prior knowledge contained in the drug-disease association data, which provided a strong basis for the research. Moreover, the previous methods only paid attention to the high-level features in the network when extracting features, and directly fused or connected them in series, resulting in the loss of information. Therefore, we propose a novel deep learning model for drug-disease association prediction, called DCNN. The model introduces the Gaussian interaction profile kernel similarity for drugs and diseases, and combines them with the structural similarity of drugs and the semantic similarity of diseases to construct the feature space jointly. Then dense convolutional neural network (DenseCNN) is used to capture the feature information of drugs and diseases, and introduces a convolutional block attention module (CBAM) to weight features from the channel and space levels to achieve adaptive optimization of features. The ten-fold cross-validation results of the model DCNN and the experimental results of the case study show that it is superior to the existing drug-disease association predictors and effectively predicts the drug-disease associations.
format	article
author	Huiqing Wang Sen Zhao Jing Zhao Zhipeng Feng
author_facet	Huiqing Wang Sen Zhao Jing Zhao Zhipeng Feng
author_sort	Huiqing Wang
title	A model for predicting drug-disease associations based on dense convolutional attention network
title_short	A model for predicting drug-disease associations based on dense convolutional attention network
title_full	A model for predicting drug-disease associations based on dense convolutional attention network
title_fullStr	A model for predicting drug-disease associations based on dense convolutional attention network
title_full_unstemmed	A model for predicting drug-disease associations based on dense convolutional attention network
title_sort	model for predicting drug-disease associations based on dense convolutional attention network
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/029eeab8870949b59f8b66cf9f166132
work_keys_str_mv	AT huiqingwang amodelforpredictingdrugdiseaseassociationsbasedondenseconvolutionalattentionnetwork AT senzhao amodelforpredictingdrugdiseaseassociationsbasedondenseconvolutionalattentionnetwork AT jingzhao amodelforpredictingdrugdiseaseassociationsbasedondenseconvolutionalattentionnetwork AT zhipengfeng amodelforpredictingdrugdiseaseassociationsbasedondenseconvolutionalattentionnetwork AT huiqingwang modelforpredictingdrugdiseaseassociationsbasedondenseconvolutionalattentionnetwork AT senzhao modelforpredictingdrugdiseaseassociationsbasedondenseconvolutionalattentionnetwork AT jingzhao modelforpredictingdrugdiseaseassociationsbasedondenseconvolutionalattentionnetwork AT zhipengfeng modelforpredictingdrugdiseaseassociationsbasedondenseconvolutionalattentionnetwork
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A model for predicting drug-disease associations based on dense convolutional attention network

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