ArticleBMC medical genomics2018
Novel link prediction for large-scale miRNA-lncRNA interaction network in a bipartite graph.
Article in BMC medical genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Enhancing LncRNA-miRNA interaction prediction with multimodal contrastive representation learning.Briefings in bioinformatics · 2025Article
- Introducing TEC-LncMir for prediction of lncRNA-miRNA interactions through deep learning of RNA sequences.Briefings in bioinformatics · 2024Article
- Predicting lncRNA-protein interactions with bipartite graph embedding and deep graph neural networks.Frontiers in genetics · 2023Article
- BoT-Net: a lightweight bag of tricks-based neural network for efficient LncRNA-miRNA interaction prediction.Interdisciplinary sciences, computational life sciences · 2022Article
- JSCSNCP-LMA: a method for predicting the association of lncRNA-miRNA.Scientific reports · 2022Article
- Cross-Domain Text Mining to Predict Adverse Events from Tyrosine Kinase Inhibitors for Chronic Myeloid Leukemia.Cancers · 2022Article
- GBDR: a Bayesian model for precise prediction of pathogenic microorganisms using 16S rRNA gene sequences.BMC genomics · 2022Article
- An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network.BMC bioinformatics · 2022Article
- Identifying and characterizing lincRNA genomic clusters reveals its cooperative functions in human cancer.Journal of translational medicine · 2021Article
- LncRNA LINC00689 Promotes the Tumorigenesis of Glioma via Mediation of miR-526b-3p/IGF2BP1 Axis.Neuromolecular medicine · 2021Article
- Graph embedding ensemble methods based on the heterogeneous network for lncRNA-miRNA interaction prediction.BMC genomics · 2020Article
- Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives.Non-coding RNA · 2020Review
- LncMirNet: Predicting LncRNA-miRNA Interaction Based on Deep Learning of Ribonucleic Acid Sequences.Molecules (Basel, Switzerland) · 2020Article
- Learning Representations to Predict Intermolecular Interactions on Large-Scale Heterogeneous Molecular Association Network.iScience · 2020Article
- Bioentity2vec: Attribute- and behavior-driven representation for predicting multi-type relationships between bioentities.GigaScience · 2020Article
- LncRNA-miRNA interaction prediction through sequence-derived linear neighborhood propagation method with information combination.BMC genomics · 2019Article
- Probing lncRNA-Protein Interactions: Data Repositories, Models, and Algorithms.Frontiers in genetics · 2019Review
- Construction and Analysis of Molecular Association Network by Combining Behavior Representation and Node Attributes.Frontiers in genetics · 2019Article
- Predicting lncRNA-miRNA InteractionFrontiers in genetics · 2019Article
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5 authors.
Funding
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Abstract
backgroundCurrent 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.
methodsIn 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.
resultsTo 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.
conclusionsGBCF could be used to select potential lncRNA targets of specific miRNAs and offer great insights for further researches on ceRNA regulation network.
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