ReviewBioMed research international2018
How Artificial Intelligence Can Improve Our Understanding of the Genes Associated with Endometriosis: Natural Language Processing of the PubMed Database.
Review in BioMed research international, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 37 citations in OpenAlex.
- Exploration of the core protein network under endometriosis symptomatology using a computational approach.Frontiers in endocrinology · 2022Pooled it
- Validation of a Semiautomated Natural Language Processing-Based Procedure for Meta-Analysis of Cancer Susceptibility Gene Penetrance.JCO clinical cancer informatics · 2019Pooled it
- Emerging Pathways to Non-Invasive Diagnosis in Endometriosis: Integrating Machine Learning, Deep Learning and Multi-Omics Biomarkers.Diagnostics (Basel, Switzerland) · 2026Review
- Inflammasomes meet organoids and artificial intelligence: unraveling the complexity of gynecological inflammation.Frontiers in immunology · 2026Review
- Prediction of genomic biomarkers for endometriosis using the transcriptomic dataset.World journal of clinical cases · 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
- Exploring the Shared Pathogenesis Mechanisms of Endometriosis and Cancer: Stemness and Targeted Treatments of Its Molecular Pathways-A Narrative Review.International journal of molecular sciences · 2024Review
- Horizons in Endometriosis: Proceedings of the Montreux Reproductive Summit, 14-15 July 2023.Facts, views & vision in ObGyn · 2024Article
- Mutational signatures for breast cancer diagnosis using artificial intelligence.Journal of the Egyptian National Cancer Institute · 2023Article
- Self-report symptom-based endometriosis prediction using machine learning.Scientific reports · 2023Article
- Evaluation of NF1 and RASA1 gene expression in endometriosis.European journal of obstetrics & gynecology and reproductive biology: X · 2022Article
- Expediting knowledge acquisition by a web framework for Knowledge Graph Exploration and Visualization (KGEV): case studies on COVID-19 and Human Phenotype Ontology.BMC medical informatics and decision making · 2022Article
- Does artificial intelligence have a role in the IVF clinic?Reproduction & fertility · 2021Article
- Augmented Reality Meets Artificial Intelligence in Robotics: A Systematic Review.Frontiers in robotics and AI · 2021Article
- Endometriosis: current challenges in modeling a multifactorial disease of unknown etiology.Journal of translational medicine · 2020Review
- The Pathogenesis of Endometriosis: Molecular and Cell Biology Insights.International journal of molecular sciences · 2019Review
- Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning.Scientific reports · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Endometriosis is a disease characterized by the development of endometrial tissue outside the uterus, but its cause remains largely unknown. Numerous genes have been studied and proposed to help explain its pathogenesis. However, the large number of these candidate genes has made functional validation through experimental methodologies nearly impossible. Computational methods could provide a useful alternative for prioritizing those most likely to be susceptibility genes. Using artificial intelligence applied to text mining, this study analyzed the genes involved in the pathogenesis, development, and progression of endometriosis. The data extraction by text mining of the endometriosis-related genes in the PubMed database was based on natural language processing, and the data were filtered to remove false positives. Using data from the text mining and gene network information as input for the web-based tool, 15,207 endometriosis-related genes were ranked according to their score in the database. Characterization of the filtered gene set through gene ontology, pathway, and network analysis provided information about the numerous mechanisms hypothesized to be responsible for the establishment of ectopic endometrial tissue, as well as the migration, implantation, survival, and proliferation of ectopic endometrial cells. Finally, the human genome was scanned through various databases using filtered genes as a seed to determine novel genes that might also be involved in the pathogenesis of endometriosis but which have not yet been characterized. These genes could be promising candidates to serve as useful diagnostic biomarkers and therapeutic targets in the management of endometriosis.
Indexed as
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What 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.