ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020
GenomeForest: An Ensemble Machine Learning Classifier for Endometriosis.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The trial behind it
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Who cites it
14 citing papers in PubMed, 25 citations in OpenAlex.
- MicroRNome analysis generates a blood-based signature for endometriosis.Scientific reports · 2022Trial
- Interpretable machine learning for endometriosis classification: a rule-based approach.BMC medical informatics and decision making · 2026Article
- Current Status and Future Potential of Machine Learning in Diagnostic Imaging of Endometriosis : A Literature Review.JNMA; journal of the Nepal Medical Association · 2025Review
- A Narrative Review Regarding Implication of Ovarian Endometriomas in Infertility.Life (Basel, Switzerland) · 2025Review
- A Comprehensive Review of Advanced Diagnostic Techniques for Endometriosis: New Approaches to Improving Women's Well-Being.Medicina (Kaunas, Lithuania) · 2024Review
- Artificial Intelligence in the Management of Women with Endometriosis and Adenomyosis: Can Machines Ever Be Worse Than Humans?Journal of clinical medicine · 2024Review
- Self-report symptom-based endometriosis prediction using machine learning.Scientific reports · 2023Article
- An online survival predictor in glioma patients using machine learning based on WHO CNS5 data.Frontiers in neurology · 2023Article
- Endometriosis in transgender men: recognizing the missing pieces.Frontiers in medicine · 2023Article
- Revisiting the Risk Factors for Endometriosis: A Machine Learning Approach.Journal of personalized medicine · 2022Article
- Endometriosis Associated-miRNome Analysis of Blood Samples: A Prospective Study.Diagnostics (Basel, Switzerland) · 2022Article
- Salivary MicroRNA Signature for Diagnosis of Endometriosis.Journal of clinical medicine · 2022Article
- Machine learning algorithms as new screening approach for patients with endometriosis.Scientific reports · 2022Article
- Computational Models for Diagnosing and Treating Endometriosis.Frontiers in reproductive health · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Endometriosis is a complex and high impact disease affecting 176 million women worldwide with diagnostic latency between 4 to 11 years due to lack of a definitive clinical symptom or a minimally invasive diagnostic method. In this study, we developed a new ensemble machine learning classifier based on chromosomal partitioning, named GenomeForest and applied it in classifying the endometriosis vs. the control patients using 38 RNA-seq and 80 enrichment-based DNA-methylation (MBD-seq) datasets, and computed performance assessment with six different experiments. The ensemble machine learning models provided an avenue for identifying several candidate biomarker genes with a very high F1 score; a near perfect F1 score (0.968) for the transcriptomics dataset and a very high F
Identifiers
32477621PMC7233069W3030731582What OpenQuestion holds
Registered trials
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.