ArticlePeerJ2021
miRcorrNet: machine learning-based integration of miRNA and mRNA expression profiles, combined with feature grouping and ranking.
Article in PeerJ, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Integrating AI and RNA biomarkers in cancer: advances in diagnostics and targeted therapies.Cell communication and signaling : CCS · 2025Review
- A Hybrid Sequential Feature Selection Approach for Identifying New Potential mRNA Biomarkers for Usher Syndrome Using Machine Learning.Biomolecules · 2025Article
- RCE-IFE: recursive cluster elimination with intra-cluster feature elimination.PeerJ. Computer science · 2025Article
- Editorial: Integrative genetics and multi-omics of complex human disorders.Frontiers in genetics · 2025Article
- Topic selection for text classification using ensemble topic modeling with grouping, scoring, and modeling approach.Scientific reports · 2024Article
- Article
- PriPath: identifying dysregulated pathways from differential gene expression via grouping, scoring, and modeling with an embedded feature selection approach.BMC bioinformatics · 2023Article
- GeNetOntology: identifying affected gene ontology terms via grouping, scoring, and modeling of gene expression data utilizing biological knowledge-based machine learning.Frontiers in genetics · 2023Article
- Invention of 3Mint for feature grouping and scoring in multi-omics.Frontiers in genetics · 2023Article
- Review
- TextNetTopics Pro, a topic model-based text classification for short text by integration of semantic and document-topic distribution information.Frontiers in genetics · 2023Article
- microBiomeGSM: the identification of taxonomic biomarkers from metagenomic data using grouping, scoring and modeling (G-S-M) approach.Frontiers in microbiology · 2023Article
- GediNET for discovering gene associations across diseases using knowledge based machine learning approach.Scientific reports · 2022Article
- miRModuleNet: Detecting miRNA-mRNA Regulatory Modules.Frontiers in genetics · 2022Article
- miRdisNET: Discovering microRNA biomarkers that are associated with diseases utilizing biological knowledge-based machine learning.Frontiers in genetics · 2022Article
- TextNetTopics: Text Classification Based Word Grouping as Topics and Topics' Scoring.Frontiers in genetics · 2022Article
- Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A better understanding of disease development and progression mechanisms at the molecular level is critical both for the diagnosis of a disease and for the development of therapeutic approaches. The advancements in high throughput technologies allowed to generate mRNA and microRNA (miRNA) expression profiles; and the integrative analysis of these profiles allowed to uncover the functional effects of RNA expression in complex diseases, such as cancer. Several researches attempt to integrate miRNA and mRNA expression profiles using statistical methods such as Pearson correlation, and then combine it with enrichment analysis. In this study, we developed a novel tool called miRcorrNet, which performs machine learning-based integration to analyze miRNA and mRNA gene expression profiles. miRcorrNet groups mRNAs based on their correlation to miRNA expression levels and hence it generates groups of target genes associated with each miRNA. Then, these groups are subject to a rank function for classification. We have evaluated our tool using miRNA and mRNA expression profiling data downloaded from The Cancer Genome Atlas (TCGA), and performed comparative evaluation with existing tools. In our experiments we show that miRcorrNet performs as good as other tools in terms of accuracy (reaching more than 95% AUC value). Additionally, miRcorrNet includes ranking steps to separate two classes, namely case and control, which is not available in other tools. We have also evaluated the performance of miRcorrNet using a completely independent dataset. Moreover, we conducted a comprehensive literature search to explore the biological functions of the identified miRNAs. We have validated our significantly identified miRNA groups against known databases, which yielded about 90% accuracy. Our results suggest that miRcorrNet is able to accurately prioritize pan-cancer regulating high-confidence miRNAs. miRcorrNet tool and all other supplementary files are available at https://github.com/malikyousef/miRcorrNet.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.