Identifying biomarkers of endometriosis using serum protein fingerprinting and artificial neural networks
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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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Cited by (37)
- Evaluation of inflammatory serum parameters as a diagnostic tool in patients with endometriosis: a case-control study 2025
- Identification of programmed cell death-related genes and diagnostic biomarkers in endometriosis using a machine learning and Mendelian randomization approach 2024
- Artificial Intelligence in the Management of Women with Endometriosis and Adenomyosis: Can Machines Ever Be Worse Than Humans? 2024
- Additional file 2 of Proteomics approach to discovering non-invasive diagnostic biomarkers and understanding the pathogenesis of endometriosis: a systematic review and meta-analysis 2024
- Additional file 1 of Proteomics approach to discovering non-invasive diagnostic biomarkers and understanding the pathogenesis of endometriosis: a systematic review and meta-analysis 2024
- Additional file 2 of Proteomics approach to discovering non-invasive diagnostic biomarkers and understanding the pathogenesis of endometriosis: a systematic review and meta-analysis 2024
- Proteomics approach to discovering non-invasive diagnostic biomarkers and understanding the pathogenesis of endometriosis: a systematic review and meta-analysis 2024
- Additional file 1 of Proteomics approach to discovering non-invasive diagnostic biomarkers and understanding the pathogenesis of endometriosis: a systematic review and meta-analysis 2024
- Assessing the Utility of artificial intelligence in endometriosis: Promises and pitfalls 2024
- Metabolic profile of follicular fluid in patients with ovarian endometriosis undergoing IVF: a pilot study 2024
- Artificial Intelligence in the Management of Women with Endometriosis and Adenomyosis: Can Machines Ever Be Worse than Humans? 2024
- Current Role of Modern Chromatography with Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy in the Investigation of Biomarkers of Endometriosis 2023
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