ArticleBMC bioinformatics2013
A novel biclustering algorithm for the discovery of meaningful biological correlations between microRNAs and their target genes.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 47 citations in OpenAlex.
- Effect of microRNA-141-3p, E2F3, CDK3, and KAT2B overexpression on histologic tumor grade and metastasis status in untreated breast cancer tissues.BioImpacts : BI · 2025Article
- Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease.Biomolecules · 2024Article
- A Multi-Level Iterative Bi-Clustering Method for Discovering miRNA Co-regulation Network of Abiotic Stress Tolerance in Soybeans.Frontiers in plant science · 2022Article
- MicroRNA-208a-3p participates in coronary heart disease by regulating the growth of hVSMCs by targeting BTG1.Experimental and therapeutic medicine · 2022Article
- Prediction of new associations between ncRNAs and diseases exploiting multi-type hierarchical clustering.BMC bioinformatics · 2020Article
- Biclustering analysis of transcriptome big data identifies condition-specific microRNA targets.Nucleic acids research · 2019Article
- Pairwise gene GO-based measures for biclustering of high-dimensional expression data.BioData mining · 2018Article
- miRNAs target databases: developmental methods and target identification techniques with functional annotations.Cellular and molecular life sciences : CMLS · 2017Review
- Identify bilayer modules via pseudo-3D clustering: applications to miRNA-gene bilayer networks.Nucleic acids research · 2016Article
- ComiRNet: a web-based system for the analysis of miRNA-gene regulatory networks.BMC bioinformatics · 2015Article
- Article
- Integrating microRNA target predictions for the discovery of gene regulatory networks: a semi-supervised ensemble learning approach.BMC bioinformatics · 2014Article
- Bioinformatics in Italy: BITS 2012, the ninth annual meeting of the Italian Society of Bioinformatics.BMC bioinformatics · 2013Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundmicroRNAs (miRNAs) are a class of small non-coding RNAs which have been recognized as ubiquitous post-transcriptional regulators. The analysis of interactions between different miRNAs and their target genes is necessary for the understanding of miRNAs' role in the control of cell life and death. In this paper we propose a novel data mining algorithm, called HOCCLUS2, specifically designed to bicluster miRNAs and target messenger RNAs (mRNAs) on the basis of their experimentally-verified and/or predicted interactions. Indeed, existing biclustering approaches, typically used to analyze gene expression data, fail when applied to miRNA:mRNA interactions since they usually do not extract possibly overlapping biclusters (miRNAs and their target genes may have multiple roles), extract a huge amount of biclusters (difficult to browse and rank on the basis of their importance) and work on similarities of feature values (do not limit the analysis to reliable interactions).
resultsTo overcome these limitations, HOCCLUS2 i) extracts possibly overlapping biclusters, to catch multiple roles of both miRNAs and their target genes; ii) extracts hierarchically organized biclusters, to facilitate bicluster browsing and to distinguish between universe and pathway-specific miRNAs; iii) extracts highly cohesive biclusters, to consider only reliable interactions; iv) ranks biclusters according to the functional similarities, computed on the basis of Gene Ontology, to facilitate bicluster analysis.
conclusionsOur results show that HOCCLUS2 is a valid tool to support biologists in the identification of context-specific miRNAs regulatory modules and in the detection of possibly unknown miRNAs target genes. Indeed, results prove that HOCCLUS2 is able to extract cohesiveness-preserving biclusters, when compared with competitive approaches, and statistically confirm (at a confidence level of 99%) that mRNAs which belong to the same biclusters are, on average, more functionally similar than mRNAs which belong to different biclusters. Finally, the hierarchy of biclusters provides useful insights to understand the intrinsic hierarchical organization of miRNAs and their potential multiple interactions on target genes.
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