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Articles Published Processes
4/9/2025 7:02:49 AM | Browse: 14 | Download: 34
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Received |
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2024-12-24 04:38 |
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Peer-Review Started |
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2024-12-24 04:38 |
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To Make the First Decision |
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Return for Revision |
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2025-02-24 01:46 |
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Revised |
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2025-03-03 11:47 |
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Second Decision |
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2025-03-13 02:35 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2025-03-13 09:12 |
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Articles in Press |
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2025-03-13 09:12 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2025-04-03 04:09 |
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Publish the Manuscript Online |
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2025-04-09 07:02 |
| ISSN |
2307-8960 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Surgery |
| Manuscript Type |
Case Control Study |
| Article Title |
Prediction of genomic biomarkers for endometriosis using the transcriptomic dataset
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Zeynep Kucukakcali, Sami Akbulut and Cemil Colak |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Sami Akbulut, MD, PhD, Professor, Surgery and Liver Transplant Institute, Inonu University Faculty of Medicine, Elazig Yolu 10. Km, Malatya 44280, Türkiye. akbulutsami@gmail.com |
| Key Words |
Endometriosis; RNA-seq; Transcriptomics; Machine learning; Classification |
| Core Tip |
Genetic research has aimed to discover the gene or genes responsible for the disease through association or linkage studies involving candidate genes or DNA mapping techniques. This study aimed to determine genomic biomarkers associated with endometriosis by using machine learning models (AdaBoost, XGBoost, Stochasting Gradient Boosting, Bagged Classification and Regression Trees). According to the variables' importance in the modeling, CUX2, CLMP, CEP131, EHD4, CDH24, ILRUN, LINC01709, HOTAIR, SLC30A2, and NKG7 genes and transcripts whose other gene names are inaccessible can be used as candidate biomarkers for endometriosis. |
| Publish Date |
2025-04-09 07:02 |
| Citation |
<p>Kucukakcali Z, Akbulut S, Colak C. Prediction of genomic biomarkers for endometriosis using the transcriptomic dataset. <i>World J Clin Cases</i> 2025; 13(20): 104556</p> |
| URL |
https://www.wjgnet.com/2307-8960/full/v13/i20/104556.htm |
| DOI |
https://dx.doi.org/10.12998/wjcc.v13.i20.104556 |
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