Identification biomarkers of eutopic endometrium in endometriosis using artificial neural networks and protein fingerprinting

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This study used artificial neural networks and protein fingerprinting to identify biomarkers in the eutopic endometrium associated with endometriosis.

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Abstract

Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) protein chip array technology was used to detect biomarkers of eutopic endometrium in endometriosis patients. Five potential biomarkers (6,898 m/z, 5,891 m/z, 5,385 m/z, 6,448 m/z, and 5,425 m/z) were found.

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Condition tags

endometriosis

MeSH descriptors

Biomarkers Endometriosis Endometrium Neural Networks, Computer Neural Networks, Computer Peptide Mapping Uterine Diseases Adult Adult Biomarkers Biomarkers Biomarkers Case-Control Studies Case-Control Studies Early Diagnosis Early Diagnosis Endometriosis Endometriosis Endometriosis Endometriosis

Citation neighborhood (2-hop)

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. Outer rings show 2-hop neighbours — papers reached through the immediate citers/citees. [ collapse to 1-hop ]

References (11)

Cited by (25)

Source provenance

europepmc
last seen: 2026-10-10T06:11:15.153948+00:00
openalex
last seen: 2026-06-10T17:14:06.276822+00:00
pubmed
last seen: 2026-10-08T21:07:11.085580+00:00
License: CC0 · commercial use OK