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How Artificial Intelligence Can Improve Our Understanding of the Genes Associated with Endometriosis: Natural Language Processing of the PubMed Database
المؤلفون المشاركون
Bouaziz, J.
Mashiach, R.
Cohen, S.
Kedem, A.
Baron, A.
Zajicek, M.
Feldman, I.
Seidman, D.
Soriano, D.
المصدر
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-7، 7ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-03-20
دولة النشر
مصر
عدد الصفحات
7
التخصصات الرئيسية
الملخص EN
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.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Bouaziz, J.& Mashiach, R.& Cohen, S.& Kedem, A.& Baron, A.& Zajicek, M.…[et al.]. 2018. How Artificial Intelligence Can Improve Our Understanding of the Genes Associated with Endometriosis: Natural Language Processing of the PubMed Database. BioMed Research International،Vol. 2018, no. 2018, pp.1-7.
https://search.emarefa.net/detail/BIM-1127785
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Bouaziz, J.…[et al.]. How Artificial Intelligence Can Improve Our Understanding of the Genes Associated with Endometriosis: Natural Language Processing of the PubMed Database. BioMed Research International No. 2018 (2018), pp.1-7.
https://search.emarefa.net/detail/BIM-1127785
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Bouaziz, J.& Mashiach, R.& Cohen, S.& Kedem, A.& Baron, A.& Zajicek, M.…[et al.]. How Artificial Intelligence Can Improve Our Understanding of the Genes Associated with Endometriosis: Natural Language Processing of the PubMed Database. BioMed Research International. 2018. Vol. 2018, no. 2018, pp.1-7.
https://search.emarefa.net/detail/BIM-1127785
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1127785
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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