Artificial neural networks combined with surface-enhanced laser desorption/ionization mass spectra distinguish endometriosis from healthy population
This study developed an artificial neural network model that successfully distinguished endometriosis patients from healthy individuals using surface-enhanced laser desorption/ionization mass spectrometry data.
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References (10)
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Cited by (17)
- Análise de marcadores protéicos no endométrio de mulheres portadoras de endometriose profunda intestinal 2019
- OMICs Studies and Endometriosis Biomarker Identification 2017
- Proteomic Biomarkers for Endometriosis 2017
- Blood biomarkers for the non-invasive diagnosis of endometriosis 2016
- The diagnosis of endometriosis with the help of mass spectrometry (a review) 2015
- Identification of biomarkers for endometriosis using clinical proteomics 2015
- Investigation of serum proteome alterations in human endometriosis 2014
- Understanding the pathogenesis of endometriosis through proteomics: recent advances and future prospects 2013
- Proteomics analysis of plasma for early diagnosis of endometriosis 2012
- Urinary peptide profiling identifies a panel of putative biomarkers for diagnosing and staging endometriosis 2011
- Diagnosis of Endometriosis: Proteomics 2011
- Proteomics a Key Tool for a Better Understanding of Endometriosis: a Mini- Review 2011
- Peripheral biomarkers of endometriosis: a systematic review 2010
- Identification biomarkers of eutopic endometrium in endometriosis using artificial neural networks and protein fingerprinting 2010
- Post-translational modifications and protein-specific isoforms in endometriosis revealed by 2D DIGE 2010
- Two novel serum biomarkers for endometriosis screened by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry and their change after laparoscopic removal of endometriosis 2008
- Identifying biomarkers of endometriosis using serum protein fingerprinting and artificial neural networks 2008
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