Prediction of miRNA-Disease Association Using Deep Collaborative Filtering.
The existing studies have shown that miRNAs are related to human diseases by regulating gene expression. Identifying miRNA association with diseases will contribute to diagnosis, treatment, and prognosis of diseases. The experimental identification of miRNA-disease associations is time-consuming, tr...
| Published in: | BioMed Research International pp. 1 - 17 |
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| Main Authors: | , |
| Format: | computer program equations & formulas research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
3/18/2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149377837&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149377837 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 3/18/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 149377837 149377837 149377837 10.1155/2021/6652948 149377837 ppf: 1 ppct: 16 formats: fmt: @attributes: type: P tig: atl: Prediction of miRNA-Disease Association Using Deep Collaborative Filtering. aug: au: Wang, Li Zhong, Cheng affil: School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China sug: subj: Disease Susceptibility Evaluation MicroRNA Prediction Models Evaluation Bioinformatics Human Genetic Techniques Neural Networks (Computer) Sensitivity and Specificity ROC Curve Algorithms ab: The existing studies have shown that miRNAs are related to human diseases by regulating gene expression. Identifying miRNA association with diseases will contribute to diagnosis, treatment, and prognosis of diseases. The experimental identification of miRNA-disease associations is time-consuming, tremendously expensive, and of high-failure rate. In recent years, many researchers predicted potential associations between miRNAs and diseases by computational approaches. In this paper, we proposed a novel method using deep collaborative filtering called DCFMDA to predict miRNA-disease potential associations. To improve prediction performance, we integrated neural network matrix factorization (NNMF) and multilayer perceptron (MLP) in a deep collaborative filtering framework. We utilized known miRNA-disease associations to capture miRNA-disease interaction features by NNMF and utilized miRNA similarity and disease similarity to extract miRNA feature vector and disease feature vector, respectively, by MLP. At last, we merged outputs of the NNMF and MLP to obtain the prediction matrix. The experimental results indicate that compared with other existing computational methods, our method can achieve the AUC of 0.9466 based on 10-fold cross-validation. In addition, case studies show that the DCFMDA can effectively predict candidate miRNAs for breast neoplasms, colon neoplasms, kidney neoplasms, leukemia, and lymphoma. pubtype: Academic Journal doctype: computer program equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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