Identifying biomarkers of endometriosis using serum protein fingerprinting and artificial neural networks

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AI-generated summary by claude@2026-06, 2026-06-08

This study identified five serum protein biomarkers using SELDI-TOF-MS and artificial neural networks, achieving 91.7% sensitivity and 90.0% specificity in distinguishing endometriosis.

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Abstract

OBJECTIVES: To use surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) protein chip array technology to detect proteomic patterns in the serum of women with endometriosis; build diagnostic models; and evaluate their clinical significance. METHODS: Serum samples from women with endometriosis and healthy women were studied using SELDI-TOF-MS protein chip technology. For every matched pair, two-thirds of the samples were used to look for different patterns and one-third was used for cross-validation. RESULTS: Five potential biomarkers were found and the diagnostic system distinguished endometriosis from validation samples with a sensitivity of 91.7% and a specificity of 90.0%. CONCLUSION: This method shows great potential in identifying biomarkers to be used for endometriosis screening.

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Condition tags

endometriosis

MeSH descriptors

Endometriosis Neural Networks, Computer Peptide Mapping Proteomics Uterine Diseases Adolescent Adult Biomarkers Biomarkers Biomarkers, Tumor Biomarkers, Tumor Endometriosis Endometriosis Female Humans Middle Aged Protein Array Analysis Protein Array Analysis Sensitivity and Specificity Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization

Citation neighborhood

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. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

References (27)

Cited by (37)

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europepmc
last seen: 2026-06-11T06:19:48.454388+00:00
openalex
last seen: 2026-06-04T00:00:01.174412+00:00
pubmed
last seen: 2026-05-13T22:14:36.758325+00:00
License: CC0 · commercial use OK