ArticleFrontiers in genetics2019
Machine Learning Classifiers for Endometriosis Using Transcriptomics and Methylomics Data.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review on the DNA methylation role in endometriosis: current evidence and perspectives.Clinical epigenetics · 2025Pooled it
- MicroRNome analysis generates a blood-based signature for endometriosis.Scientific reports · 2022Trial
- Prediction of genomic biomarkers for endometriosis using the transcriptomic dataset.World journal of clinical cases · 2025Article
- Evaluation of inflammatory serum parameters as a diagnostic tool in patients with endometriosis: a case-control study.Scientific reports · 2025Article
- Current Status and Future Potential of Machine Learning in Diagnostic Imaging of Endometriosis : A Literature Review.JNMA; journal of the Nepal Medical Association · 2025Review
- Molecular Basis of Impaired Decidualization in the Eutopic Endometrium of Endometriosis Patients.Cells · 2025Review
- Improving Replication in Endometrial Omics: Understanding the Influence of the Menstrual Cycle.International journal of molecular sciences · 2025Review
- Identification of Important Diagnostic Genes in the Uterine Using Bioinformatics and Machine Learning.Medical journal of the Islamic Republic of Iran · 2025Article
- Complement and coagulation cascade cross-talk in endometriosis and the potential of Janus Kinase inhibitors-a network meta-analysis.Frontiers in immunology · 2025Article
- Artificial Intelligence in the Management of Women with Endometriosis and Adenomyosis: Can Machines Ever Be Worse Than Humans?Journal of clinical medicine · 2024Review
- Identification of potential diagnostic biomarkers and therapeutic targets for endometriosis based on bioinformatics and machine learning analysis.Journal of assisted reproduction and genetics · 2023Article
- Endometriosis in the era of precision medicine and impact on sexual and reproductive health across the lifespan and in diverse populations.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2023Review
- Self-report symptom-based endometriosis prediction using machine learning.Scientific reports · 2023Article
- Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer.Sensors (Basel, Switzerland) · 2023Article
- Construction and evaluation of endometriosis diagnostic prediction model and immune infiltration based on efferocytosis-related genes.Frontiers in molecular biosciences · 2023Article
- Endometriosis in transgender men: recognizing the missing pieces.Frontiers in medicine · 2023Article
- Predicting embryonic aneuploidy rate in IVF patients using whole-exome sequencing.Human genetics · 2022Article
- Article
- Endometriosis Associated-miRNome Analysis of Blood Samples: A Prospective Study.Diagnostics (Basel, Switzerland) · 2022Article
- Machine learning model for predicting Major Depressive Disorder using RNA-Seq data: optimization of classification approach.Cognitive neurodynamics · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Endometriosis is a complex and common gynecological disorder yet a poorly understood disease affecting about 176 million women worldwide and causing significant impact on their quality of life and economic burden. Neither a definitive clinical symptom nor a minimally invasive diagnostic method is available, thus leading to an average of 4 to 11 years of diagnostic latency. Discovery of relevant biological patterns from microarray expression or next generation sequencing (NGS) data has been advanced over the last several decades by applying various machine learning tools. We performed machine learning analysis using 38 RNA-seq and 80 enrichment-based DNA methylation (MBD-seq) datasets. We experimented how well various supervised machine learning methods such as decision tree, partial least squares discriminant analysis (PLSDA), support vector machine, and random forest perform in classifying endometriosis from the control samples trained on both transcriptomics and methylomics data. The assessment was done from two different perspectives for improving classification performances: a) implication of three different normalization techniques and b) implication of differential analysis using the generalized linear model (GLM). Several candidate biomarker genes were identified by multiple machine learning experiments including
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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.