ArticleBMC bioinformatics2018
Hierarchical structural component modeling of microRNA-mRNA integration analysis.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A Mathematical Approach on the Limits of ceRNA Hypothesis Through an Ordinary Differential Equations (ODE) Model of mRNA-microRNA Interactions.Non-coding RNA · 2026Article
- Hierarchical structural component model for pathway analysis of multinomial phenotypes.Genes & genomics · 2025Article
- Enhanced adaptive permutation test with negative binomial distribution in genome-wide omics datasets.Genes & genomics · 2025Article
- Identifying miRNA-mRNA Integration Set Associated With Survival Time.Frontiers in genetics · 2021Article
- Article
- Frequency of MicroRNA Response Elements Identifies Pathologically Relevant Signaling Pathways in Triple-Negative Breast Cancer.iScience · 2020Article
- Enhanced Permutation Tests via Multiple Pruning.Frontiers in genetics · 2020Article
- HisCoM-PAGE: software for hierarchical structural component models for pathway analysis of gene expression data.Genomics & informatics · 2019Article
- HisCoM-PAGE: Hierarchical Structural Component Models for Pathway Analysis of Gene Expression Data.Genes · 2019Article
- HisCoM-mimi: Software for Hierarchical Structural Component Analysis for miRNA-mRNA Integration Model for Binary Phenotypes.Genomics & informatics · 2019Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentification of multi-markers is one of the most challenging issues in personalized medicine era. Nowadays, many different types of omics data are generated from the same subject. Although many methods endeavor to identify candidate markers, for each type of omics data, few or none can facilitate such identification.
resultsIt is well known that microRNAs affect phenotypes only indirectly, through regulating mRNA expression and/or protein translation. Toward addressing this issue, we suggest a hierarchical structured component analysis of microRNA-mRNA integration ("HisCoM-mimi") model that accounts for this biological relationship, to efficiently study and identify such integrated markers. In simulation studies, HisCoM-mimi showed the better performance than the other three methods. Also, in real data analysis, HisCoM-mimi successfully identified more gives more informative miRNA-mRNA integration sets relationships for pancreatic ductal adenocarcinoma (PDAC) diagnosis, compared to the other methods.
conclusionAs exemplified by an application to pancreatic cancer data, our proposed model effectively identified integrated miRNA/target mRNA pairs as markers for early diagnosis, providing a much broader biological interpretation.
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