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|Title:||Novel link prediction for large-scale miRNA-lncRNA interaction network in a bipartite graph||Authors:||Huang, ZA
|Issue Date:||2018||Publisher:||BioMed Central||Source:||BMC medical genomics, 31 Dec. 2018, v. 11, suppl. 6, 113, p. 1-11 How to cite?||Journal:||BMC medical genomics||Abstract:||Background: Current knowledge and data on miRNA-lncRNA interactions is still limited and little effort has been made to predict target lncRNAs of miRNAs. Accumulating evidences suggest that the interaction patterns between lncRNAs and miRNAs are closely related to relative expression level, forming a titration mechanism. It could provide an effective approach for characteristic feature extraction. In addition, using the coding non-coding co-expression network and sequence data could also help to measure the similarities among miRNAs and lncRNAs. By mathematically analyzing these types of similarities, we come up with two findings that (i) lncRNAs/miRNAs tend to collaboratively interact with miRNAs/lncRNAs of similar expression profiles, and vice versa, and (ii) those miRNAs interacting with a cluster of common target genes tend to jointly target at the common lncRNAs.
Methods: In this work, we developed a novel group preference Bayesian collaborative filtering model called GBCF for picking up a top-k probability ranking list for an individual miRNA or lncRNA based on the known miRNA-lncRNA interaction network.
Results: To evaluate the effectiveness of GBCF, leave-one-out and k-fold cross validations as well as a series of comparison experiments were carried out. GBCF achieved the values of area under ROC curve of 0.9193, 0.8354+/-0.0079, 0.8615+/-0.0078, and 0.8928+/-0.0082 based on leave-one-out, 2-fold, 5-fold, and 10-fold cross validations respectively, demonstrating its reliability and robustness.
Conclusions: GBCF could be used to select potential lncRNA targets of specific miRNAs and offer great insights for further researches on ceRNA regulation network.
|Description:||29th International Conference on Genome Informatics (GIW 2018): Medical Genomics, Kunming, Yunnan, China, Dec 3-5, 2018||URI:||http://hdl.handle.net/10397/80496||EISSN:||1755-8794||DOI:||10.1186/s12920-018-0429-8||Rights:||© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/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 Huang, Z. A., Huang, Y. A., You, Z. H., Zhu, Z. X., & Sun, Y. W. (2018). Novel link prediction for large-scale miRNA-lncRNA interaction network in a bipartite graph. BMC Medical Genomics, 11(suppl. 6), 113, 1-11 is available at https://dx.doi.org/10.1186/s12920-018-0429-8
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