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BioMed Research International
Volume 2018 (2018), Article ID 6217812, 7 pages
https://doi.org/10.1155/2018/6217812
Review Article

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

1Department of Obstetrics and Gynecology, The Chaim Sheba Medical Center, Ramat Gan, Israel
2Artichoc Institue, 5 Alkalay, Tel Aviv, Israel
3Department of Ophthalmology, Ichilov Hospital, Tel Aviv, Israel

Correspondence should be addressed to J. Bouaziz; moc.liamg@zizauobemorej.rd

Received 15 December 2017; Accepted 15 February 2018; Published 20 March 2018

Academic Editor: Marco Scioscia

Copyright © 2018 J. Bouaziz et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.