ArticleMolecular oncology2019
Topologically inferring active miRNA-mediated subpathways toward precise cancer classification by directed random walk.
Article in Molecular oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 7 citations in OpenAlex.
- Computational Methods for Pathway and Network Analysis with MicroRNAs.Methods in molecular biology (Clifton, N.J.) · 2026Review
- Data analysis methods for defining biomarkers from omics data.Analytical and bioanalytical chemistry · 2022Review
- Identification of miRNA-Mediated Subpathways as Prostate Cancer Biomarkers Based on Topological Inference in a Machine Learning Process Using Integrated Gene and miRNA Expression Data.Frontiers in genetics · 2021Article
- Machine Learning-Based Ensemble Recursive Feature Selection of Circulating miRNAs for Cancer Tumor Classification.Cancers · 2020Article
- MiRNA-Mediated Subpathway Identification and Network Module Analysis to Reveal Prognostic Markers in Human Pancreatic Cancer.Frontiers in genetics · 2020Article
- Corrigendum to: Topologically inferring active miRNA-mediated subpathways toward precise cancer classification by directed random walk.Molecular oncology · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
13 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate predictions of classification biomarkers and disease status are indispensable for clinical cancer diagnosis and research. However, the robustness of conventional gene biomarkers is limited by issues with reproducibility across different measurement platforms and cohorts of patients. In this study, we collected 4775 samples from 12 different cancer datasets, which contained 4636 TCGA samples and 139 GEO samples. A new method was developed to detect miRNA-mediated subpathway activities by using directed random walk (miDRW). To calculate the activity of each miRNA-mediated subpathway, we constructed a global directed pathway network (GDPN) with genes as nodes. We then identified miRNAs with expression levels which were strongly inversely correlated with differentially expressed target genes in the GDPN. Finally, each miRNA-mediated subpathway activity was integrated with the topological information, differential levels of miRNAs and genes, expression levels of genes, and target relationships between miRNAs and genes. The results showed that the proposed method yielded a more robust and accurate overall performance compared with other existing pathway-based, miRNA-based, and gene-based classification methods. The high-frequency miRNA-mediated subpathways are more reliable in classifying samples and for selecting therapeutic strategies.
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