ArticleFrontiers in digital health2023
Identifying potential circulating miRNA biomarkers for the diagnosis and prediction of ovarian cancer using machine-learning approach: application of Boruta.
Article in Frontiers in digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- AI-Derived Blood Biomarkers for Ovarian Cancer Diagnosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Potential of RAD51C as a hub gene and therapeutic target in bladder cancer: insights from bioinformatics and experimental analysis.International journal of surgery (London, England) · 2026Article
- MicroRNAs in oncology: a translational perspective in the era of AI.Nature reviews. Clinical oncology · 2026Review
- Exploring a plasma proteomic biosignature associated with cardiac involvement in Fabry disease.Journal of molecular medicine (Berlin, Germany) · 2026Article
- MetaPaCS: A Novel Meta-Learning Framework for Pancreatic Cancer Subtype Identification.bioRxiv : the preprint server for biology · 2026Article
- S12micro: a machine learning-derived serum miRNA panel for high-performance pan-cancer diagnosis via liquid biopsy.International journal of surgery (London, England) · 2026Article
- Urinary microRNAs for the non-invasive diagnosis of endometriosis identified by next-generation sequencing and machine learning.Reproductive biology and endocrinology : RB&E · 2026Article
- Glioma identification from microRNA biomarkers using machine learning.Frontiers in systems biology · 2026Article
- Multi-omics mapping of PSC genetic risk to a high TWAS-active JAML+ lipid-associated macrophage program: bridging single-cell heterogeneity, spatial fibrotic niches, and machine learning selection.Frontiers in cell and developmental biology · 2026Article
- Multi-omics Mendelian randomization identifies mitochondrial genes associated with immune microenvironment signatures in endometriosis.Frontiers in reproductive health · 2026Article
- Crosstalk between microRNA and oxidative stress in ovarian cancer: diagnosis, pathogenesis and therapeutic resistance.Medical oncology (Northwood, London, England) · 2025Review
- Decision tree-based machine learning methods for identifying colorectal cancer-associated microRNA signatures and their regulatory networks.Scientific reports · 2025Article
- Prediction of parastomal hernia in patients undergoing preventive ostomy after rectal cancer resection using machine learning.World journal of gastrointestinal surgery · 2025Article
- Functional brain network identification in opioid use disorder using machine learning analysis of resting-state fMRI BOLD signals.Computers in biology and medicine · 2025Article
- Machine learning identifies KRT8 dysregulation and endothelial remodeling in Moyamoya disease.Scientific reports · 2025Article
- Machine learning in ovarian cancer: a bibliometric and visual analysis from 2004 to 2024.Discover oncology · 2025Article
- Integration of Bioinformatics and Machine Learning Strategies Identifies Ferroptosis and Immune Infiltration Signatures in Peri-Implantitis.International journal of molecular sciences · 2025Article
- Article
- Explainable AI-based feature importance analysis for ovarian cancer classification with ensemble methods.Frontiers in public health · 2025Article
- Risk of intraoperative hemorrhage during cesarean scar ectopic pregnancy surgery: development and validation of an interpretable machine learning prediction model.EClinicalMedicine · 2024Article
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Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: In gynecologic oncology, ovarian cancer is a great clinical challenge. Because of the lack of typical symptoms and effective biomarkers for noninvasive screening, most patients develop advanced-stage ovarian cancer by the time of diagnosis. MicroRNAs (miRNAs) are a type of non-coding RNA molecule that has been linked to human cancers. Specifying diagnostic biomarkers to determine non-cancer and cancer samples is difficult. Methods: By using Boruta, a novel random forest-based feature selection in the machine-learning techniques, we aimed to identify biomarkers associated with ovarian cancer using cancerous and non-cancer samples from the Gene Expression Omnibus (GEO) database: GSE106817. In this study, we used two independent GEO data sets as external validation, including GSE113486 and GSE113740. We utilized five state-of-the-art machine-learning algorithms for classification: logistic regression, random forest, decision trees, artificial neural networks, and XGBoost. Results: Four models discovered in GSE113486 had an AUC of 100%, three in GSE113740 with AUC of over 94%, and four in GSE113486 with AUC of over 94%. We identified 10 miRNAs to distinguish ovarian cancer cases from normal controls: hsa-miR-1290, hsa-miR-1233-5p, hsa-miR-1914-5p, hsa-miR-1469, hsa-miR-4675, hsa-miR-1228-5p, hsa-miR-3184-5p, hsa-miR-6784-5p, hsa-miR-6800-5p, and hsa-miR-5100. Our findings suggest that miRNAs could be used as possible biomarkers for ovarian cancer screening, for possible intervention.
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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.