How Artificial Intelligence Can Improve Our Understanding of the Genes Associated with Endometriosis: Natural Language Processing of the PubMed Database

Jérôme Bouaziz, R Mashiach, S. I. Cohen, S Cohen, A Kedem, Alon Kedem, A Baron, Alexandra Baron, M Zajicek, Ilan Feldman, I Feldman, D Seidman, Daniel S. Seidman, D Soriano, David Soriano
BioMed research international · 2018 · vol. 2018 , pp. 1–7 · doi:10.1155/2018/6217812 · PMID:29750165 · W2793023850
review OA: hybrid CC0 ⤵ 15 in-corpus citations
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⚙ AI-generated summary by claude@2026-06, 2026-06-07 ⓘ

This study used AI-driven natural language processing of PubMed to analyze 15,207 endometriosis-related genes, identifying potential diagnostic biomarkers and therapeutic targets.

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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.

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Condition tags

mesh:D004715endometriosis

MeSH descriptors

Endometriosis Artificial Intelligence Databases, Factual Endometriosis Endometriosis Endometrium Endometrium Female Gene Regulatory Networks Gene Regulatory Networks Humans Natural Language Processing PubMed

Citation neighborhood (2-hop)

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. Outer rings show 2-hop neighbours — papers reached through the immediate citers/citees. [ collapse to 1-hop ]

References (28)

Cited by (15)

Source provenance

europepmc
last seen: 2026-06-04T01:30:01.192114+00:00
openalex
last seen: 2026-06-04T00:00:01.174412+00:00
pubmed
last seen: 2026-05-13T22:19:49.066213+00:00
License: CC0 · commercial use OK