Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81738
Title: Predicting drug-disease associations via sigmoid kernel-based convolutional neural networks
Authors: Jiang, HJ
You, TH
Huang, YA 
Keywords: Sigmoid kernel
Convolutional neural networks
Random forest
Issue Date: 2019
Publisher: BioMed Central
Source: Journal of translational medicine, 20 Nov. 2019, v. 17, 382, p. 1-11 How to cite?
Journal: Journal of translational medicine 
Abstract: Background 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.
Methods 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.
Results 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.
Conclusion 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.
URI: http://hdl.handle.net/10397/81738
EISSN: 1479-5876
DOI: 10.1186/s12967-019-2127-5
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.
The 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/s12967-019-2127-5
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