Novel link prediction for large-scale miRNA-lncRNA interaction network in a bipartite graph

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Article number113
Journal / PublicationBMC Medical Genomics
Volume11
Issue numberSuppl 6
Online published31 Dec 2018
Publication statusPublished - 2018

Conference

Title29th International Conference on Genome Informatics (GIW 2018)
LocationKunming University of Science and Technology
PlaceChina
CityYunnan
Period3 - 5 December 2018

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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.

Research Area(s)

  • ceRNA network, Collaborative filtering, Computational prediction, Expression profile, miRNA-lncRNA interaction

Citation Format(s)

Novel link prediction for large-scale miRNA-lncRNA interaction network in a bipartite graph. / Huang, Zhi-An; Huang, Yu-An; You, Zhu-Hong; Zhu, Zexuan; Sun, Yiwen.

In: BMC Medical Genomics, Vol. 11, No. Suppl 6, 113, 2018.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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