Predicting Drug-Disease Associations via Using Gaussian Interaction Profile and Kernel-Based Autoencoder.
Computational 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 le...
| Publicado en: | BioMed Research International pp. 1 - 12 |
|---|---|
| Autores principales: | , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
8/27/2019
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=138290878&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138290878 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/27/2019 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 138290878 138290878 138290878 10.1155/2019/2426958 138290878 ppf: 1 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Predicting Drug-Disease Associations via Using Gaussian Interaction Profile and Kernel-Based Autoencoder. aug: au: Jiang, Han-Jing Huang, Yu-An You, Zhu-Hong affil: Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China sug: subj: Neural Networks (Computer) Methods Machine Learning Methods Autoencoder Utilization Bioinformatics Methods Drug Design Methods Human Drug Design Economics Obesity Drug Therapy Alzheimer's Disease Drug Therapy ab: Computational 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|