Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81653
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dc.contributorDepartment of Computing-
dc.creatorJiang, HJ-
dc.creatorHuang, YA-
dc.creatorYou, ZH-
dc.date.accessioned2020-02-10T12:28:26Z-
dc.date.available2020-02-10T12:28:26Z-
dc.identifier.issn2314-6133-
dc.identifier.urihttp://hdl.handle.net/10397/81653-
dc.language.isoenen_US
dc.publisherHindawi Publishing Corporationen_US
dc.rightsCopyright © 2019 Han-Jing Jiang et al. This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.rightsThe following publication Han-Jing Jiang, Yu-An Huang, and Zhu-Hong You, “Predicting Drug-Disease Associations via Using Gaussian Interaction Profile and Kernel-Based Autoencoder,” BioMed Research International, vol. 2019, Article ID 2426958, 11 pages, 2019 is available at https://dx.doi.org/10.1155/2019/2426958en_US
dc.titlePredicting drug-disease associations via using Gaussian interaction profile and kernel-based autoencoderen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1-
dc.identifier.epage11-
dc.identifier.volume2019-
dc.identifier.doi10.1155/2019/2426958-
dcterms.abstractComputational 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.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationBioMed research international, 27 Aug. 2019, v. 2019, 2426958, p. 1-11-
dcterms.isPartOfBioMed research international-
dcterms.issued2019-
dc.identifier.isiWOS:000486402800003-
dc.identifier.eissn2314-6141-
dc.identifier.artn2426958-
dc.description.validate202002 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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