Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81738
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dc.contributorDepartment of Computing-
dc.creatorJiang, HJ-
dc.creatorYou, TH-
dc.creatorHuang, YA-
dc.date.accessioned2020-02-10T12:28:55Z-
dc.date.available2020-02-10T12:28:55Z-
dc.identifier.urihttp://hdl.handle.net/10397/81738-
dc.language.isoenen_US
dc.publisherBioMed Centralen_US
dc.rights© The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.en_US
dc.rightsThe following publication Jiang, H., You, Z. & Huang, Y. Predicting drug−disease associations via sigmoid kernel-based convolutional neural networks. J Transl Med 17, 382, 1-11 is available at https://dx.doi.org/10.1186/s12967-019-2127-5en_US
dc.subjectSigmoid kernelen_US
dc.subjectConvolutional neural networksen_US
dc.subjectRandom foresten_US
dc.titlePredicting drug-disease associations via sigmoid kernel-based convolutional neural networksen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1-
dc.identifier.epage11-
dc.identifier.volume17-
dc.identifier.doi10.1186/s12967-019-2127-5-
dcterms.abstractBackground In the process of drug development, computational drug repositioning is effective and resource-saving with regards to its important functions on identifying new drug-disease associations. Recent years have witnessed a great progression in the field of data mining with the advent of deep learning. An increasing number of deep learning-based techniques have been proposed to develop computational tools in bioinformatics.-
dcterms.abstractMethods Along this promising direction, we here propose a drug repositioning computational method combining the techniques of Sigmoid Kernel and Convolutional Neural Network (SKCNN) which is able to learn new features effectively representing drug-disease associations via its hidden layers. Specifically, we first construct similarity metric of drugs using drug sigmoid similarity and drug structural similarity, and that of disease using disease sigmoid similarity and disease semantic similarity. Based on the combined similarities of drugs and diseases, we then use SKCNN to learn hidden representations for each drug-disease pair whose labels are finally predicted by a classifier based on random forest.-
dcterms.abstractResults A series of experiments were implemented for performance evaluation and their results show that the proposed SKCNN improves the prediction accuracy compared with other state-of-the-art approaches. Case studies of two selected disease are also conducted through which we prove the superior performance of our method in terms of the actual discovery of potential drug indications.-
dcterms.abstractConclusion The aim of this study was to establish an effective predictive model for finding new drug-disease associations. These experimental results show that SKCNN can effectively predict the association between drugs and diseases.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of translational medicine, 20 Nov. 2019, v. 17, 382, p. 1-11-
dcterms.isPartOfJournal of translational medicine-
dcterms.issued2019-
dc.identifier.isiWOS:000498704300003-
dc.identifier.scopus2-s2.0-85075518287-
dc.identifier.pmid31747915-
dc.identifier.eissn1479-5876-
dc.identifier.artn382-
dc.description.validate202002 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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