HAMDA: Hybrid Approach for MiRNA-Disease Association prediction.
For decades, enormous experimental researches have collectively indicated that microRNA (miRNA) could play indispensable roles in many critical biological processes and thus also the pathogenesis of human complex diseases. Whereas the resource and time cost required in traditional biology experiment...
| Publicado en: | Journal of Biomedical Informatics Vol. 76; pp. 50 - 59 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Dec2017
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| 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=127986486&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 127986486 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Dec2017 vid: 76 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 127986486 127986486 NLM29097278 127986486 10.1016/j.jbi.2017.10.014 NLM29097278 127986486 ppf: 50 ppct: 9 formats: tig: atl: HAMDA: Hybrid Approach for MiRNA-Disease Association prediction. aug: au: Chen, Xing Niu, Ya-Wei Wang, Guang-Hui Yan, Gui-Ying affil: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China sug: subj: Disease Susceptibility Computer Simulation RNA Neoplasms Algorithms ab: For decades, enormous experimental researches have collectively indicated that microRNA (miRNA) could play indispensable roles in many critical biological processes and thus also the pathogenesis of human complex diseases. Whereas the resource and time cost required in traditional biology experiments are expensive, more and more attentions have been paid to the development of effective and feasible computational methods for predicting potential associations between disease and miRNA. In this study, we developed a computational model of Hybrid Approach for MiRNA-Disease Association prediction (HAMDA), which involved the hybrid graph-based recommendation algorithm, to reveal novel miRNA-disease associations by integrating experimentally verified miRNA-disease associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity into a recommendation algorithm. HAMDA took not only network structure and information propagation but also node attribution into consideration, resulting in a satisfactory prediction performance. Specifically, HAMDA obtained AUCs of 0.9035 and 0.8395 in the frameworks of global and local leave-one-out cross validation, respectively. Meanwhile, HAMDA also achieved good performance with AUC of 0.8965 ± 0.0012 in 5-fold cross validation. Additionally, we conducted case studies about three important human cancers for performance evaluation of HAMDA. As a result, 90% (Lymphoma), 86% (Prostate Cancer) and 92% (Kidney Cancer) of top 50 predicted miRNAs were confirmed by recent experiment literature, which showed the reliable prediction ability of HAMDA. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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