Prediction of genomic biomarkers for endometriosis using the transcriptomic dataset

This study determined possible genomic biomarkers for endometriosis using transcriptomic data from patients with/without endometriosis. The applied ML model successfully classified endometriosis and created a highly accurate diagnostic prediction model. Future genomic studies could explain the underlying pathology of endometriosis, and a non-invasive diagnostic method could replace the invasive ones.

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Publication Details

Journal
World Journal of Clinical Cases
Published
2025-04-08
DOI
https://doi.org/10.12998/wjcc.v13.i20.104556
Citations
2
Primary Topic
Endometriosis Research and Treatment
Type
article
Field-Weighted Citation Impact
3.40
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article

Prediction of genomic biomarkers for endometriosis using the transcriptomic dataset

Zeynep Kucukakcali, Sami Akbulut, Cemil Çolak
2 citations
World Journal of Clinical Cases
Endometriosis Research and Treatment
3.40
article

Prediction of genomic biomarkers for endometriosis using the transcriptomic dataset

Zeynep Kucukakcali, Sami Akbulut, Cemil Çolak
article en
2 citations

Abstract

This study determined possible genomic biomarkers for endometriosis using transcriptomic data from patients with/without endometriosis. The applied ML model successfully classified endometriosis and created a highly accurate diagnostic prediction model. Future genomic studies could explain the underlying pathology of endometriosis, and a non-invasive diagnostic method could replace the invasive ones.

World Journal of Clinical CasesVol. 13(20)
Inonu University (TR)
Good health and well-being
Openalex Percentile: Top 9%
Endometriosis Research and Treatment
3.40
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