Cargando…
A Joint Model of Random Forest and Artificial Neural Network for the Diagnosis of Endometriosis
Endometriosis (EM), an estrogen-dependent inflammatory disease with unknown etiology, affects thousands of childbearing-age couples, and its early diagnosis is still very difficult. With the rapid development of sequencing technology in recent years, the accumulation of many sequencing data makes it...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8957986/ https://www.ncbi.nlm.nih.gov/pubmed/35350240 http://dx.doi.org/10.3389/fgene.2022.848116 |
_version_ | 1784676852167606272 |
---|---|
author | She, Jiajie Su, Danna Diao, Ruiying Wang, Liping |
author_facet | She, Jiajie Su, Danna Diao, Ruiying Wang, Liping |
author_sort | She, Jiajie |
collection | PubMed |
description | Endometriosis (EM), an estrogen-dependent inflammatory disease with unknown etiology, affects thousands of childbearing-age couples, and its early diagnosis is still very difficult. With the rapid development of sequencing technology in recent years, the accumulation of many sequencing data makes it possible to screen important diagnostic biomarkers from some EM-related genes. In this study, we utilized public datasets in the Gene Expression Omnibus (GEO) and Array-Express database and identified seven important differentially expressed genes (DEGs) (COMT, NAA16, CCDC22, EIF3E, AHI1, DMXL2, and CISD3) through the random forest classifier. Among these DEGs, AHI1, DMXL2, and CISD3 have never been reported to be associated with the pathogenesis of EMs. Our study indicated that these three genes might participate in the pathogenesis of EMs through oxidative stress, epithelial–mesenchymal transition (EMT) with the activation of the Notch signaling pathway, and mitochondrial homeostasis, respectively. Then, we put these seven DEGs into an artificial neural network to construct a novel diagnostic model for EMs and verified its diagnostic efficacy in two public datasets. Furthermore, these seven DEGs were included in 15 hub genes identified from the constructed protein–protein interaction (PPI) network, which confirmed the reliability of the diagnostic model. We hope the diagnostic model can provide novel sights into the understanding of the pathogenesis of EMs and contribute to the clinical diagnosis and treatment of EMs. |
format | Online Article Text |
id | pubmed-8957986 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89579862022-03-28 A Joint Model of Random Forest and Artificial Neural Network for the Diagnosis of Endometriosis She, Jiajie Su, Danna Diao, Ruiying Wang, Liping Front Genet Genetics Endometriosis (EM), an estrogen-dependent inflammatory disease with unknown etiology, affects thousands of childbearing-age couples, and its early diagnosis is still very difficult. With the rapid development of sequencing technology in recent years, the accumulation of many sequencing data makes it possible to screen important diagnostic biomarkers from some EM-related genes. In this study, we utilized public datasets in the Gene Expression Omnibus (GEO) and Array-Express database and identified seven important differentially expressed genes (DEGs) (COMT, NAA16, CCDC22, EIF3E, AHI1, DMXL2, and CISD3) through the random forest classifier. Among these DEGs, AHI1, DMXL2, and CISD3 have never been reported to be associated with the pathogenesis of EMs. Our study indicated that these three genes might participate in the pathogenesis of EMs through oxidative stress, epithelial–mesenchymal transition (EMT) with the activation of the Notch signaling pathway, and mitochondrial homeostasis, respectively. Then, we put these seven DEGs into an artificial neural network to construct a novel diagnostic model for EMs and verified its diagnostic efficacy in two public datasets. Furthermore, these seven DEGs were included in 15 hub genes identified from the constructed protein–protein interaction (PPI) network, which confirmed the reliability of the diagnostic model. We hope the diagnostic model can provide novel sights into the understanding of the pathogenesis of EMs and contribute to the clinical diagnosis and treatment of EMs. Frontiers Media S.A. 2022-03-08 /pmc/articles/PMC8957986/ /pubmed/35350240 http://dx.doi.org/10.3389/fgene.2022.848116 Text en Copyright © 2022 She, Su, Diao and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics She, Jiajie Su, Danna Diao, Ruiying Wang, Liping A Joint Model of Random Forest and Artificial Neural Network for the Diagnosis of Endometriosis |
title | A Joint Model of Random Forest and Artificial Neural Network for the Diagnosis of Endometriosis |
title_full | A Joint Model of Random Forest and Artificial Neural Network for the Diagnosis of Endometriosis |
title_fullStr | A Joint Model of Random Forest and Artificial Neural Network for the Diagnosis of Endometriosis |
title_full_unstemmed | A Joint Model of Random Forest and Artificial Neural Network for the Diagnosis of Endometriosis |
title_short | A Joint Model of Random Forest and Artificial Neural Network for the Diagnosis of Endometriosis |
title_sort | joint model of random forest and artificial neural network for the diagnosis of endometriosis |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8957986/ https://www.ncbi.nlm.nih.gov/pubmed/35350240 http://dx.doi.org/10.3389/fgene.2022.848116 |
work_keys_str_mv | AT shejiajie ajointmodelofrandomforestandartificialneuralnetworkforthediagnosisofendometriosis AT sudanna ajointmodelofrandomforestandartificialneuralnetworkforthediagnosisofendometriosis AT diaoruiying ajointmodelofrandomforestandartificialneuralnetworkforthediagnosisofendometriosis AT wangliping ajointmodelofrandomforestandartificialneuralnetworkforthediagnosisofendometriosis AT shejiajie jointmodelofrandomforestandartificialneuralnetworkforthediagnosisofendometriosis AT sudanna jointmodelofrandomforestandartificialneuralnetworkforthediagnosisofendometriosis AT diaoruiying jointmodelofrandomforestandartificialneuralnetworkforthediagnosisofendometriosis AT wangliping jointmodelofrandomforestandartificialneuralnetworkforthediagnosisofendometriosis |