title: Tear fluid proteomics multimarkers for diabetic retinopathy screening.
abstract: BACKGROUND
  The aim of the project was to develop a novel method for diabetic retinopathy screening based on the examination of tear fluid biomarker changes. In order to evaluate the usability of protein biomarkers for pre-screening purposes several different approaches were used, including machine learning algorithms.
  
  
  METHODS
  All persons involved in the study had diabetes. Diabetic retinopathy (DR) was diagnosed by capturing 7-field fundus images, evaluated by two independent ophthalmologists. 165 eyes were examined (from 119 patients), 55 were diagnosed healthy and 110 images showed signs of DR. Tear samples were taken from all eyes and state-of-the-art nano-HPLC coupled ESI-MS/MS mass spectrometry protein identification was performed on all samples. Applicability of protein biomarkers was evaluated by six different optimally parameterized machine learning algorithms: Support Vector Machine, Recursive Partitioning, Random Forest, Naive Bayes, Logistic Regression, K-Nearest Neighbor.
  
  
  RESULTS
  Out of the six investigated machine learning algorithms the result of Recursive Partitioning proved to be the most accurate. The performance of the system realizing the above algorithm reached 74% sensitivity and 48% specificity.
  
  
  CONCLUSIONS
  Protein biomarkers selected and classified with machine learning algorithms alone are at present not recommended for screening purposes because of low specificity and sensitivity values. This tool can be potentially used to improve the results of image processing methods as a complementary tool in automatic or semiautomatic systems.
authors.affiliation: Department of Computer Graphics and Image Processing, University of Debrecen, Faculty of Informatics, Debrecen, Hungary. zsolt.torok@astridbio.com
authors.forename: Zsolt
authors.initials: Z
authors.lastname: Torok
authors.forename: Tunde
authors.initials: T
authors.lastname: Peto
authors.forename: Eva
authors.initials: E
authors.lastname: Csosz
authors.forename: Edit
authors.initials: E
authors.lastname: Tukacs
authors.forename: Agnes
authors.initials: A
authors.lastname: Molnar
authors.forename: Zsuzsanna
authors.initials: Z
authors.lastname: Maros-Szabo
authors.forename: Andras
authors.initials: A
authors.lastname: Berta
authors.forename: Jozsef
authors.initials: J
authors.lastname: Tozser
authors.forename: Andras
authors.initials: A
authors.lastname: Hajdu
authors.forename: Valeria
authors.initials: V
authors.lastname: Nagy
authors.forename: Balint
authors.initials: B
authors.lastname: Domokos
authors.forename: Adrienne
authors.initials: A
authors.lastname: Csutak
mesh_terms: D000328:Adult; D000465:Algorithms; D015415:Biomarkers / Q000378:metabolism; D003930:Diabetic Retinopathy / Q000175:diagnosis* / Q000378:metabolism; D005136:Eye Proteins / Q000378:metabolism*; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D012680:Sensitivity and Specificity; D053719:Tandem Mass Spectrometry; D013666:Tears / Q000378:metabolism*
chemical_list: D015415:Biomarkers; D005136:Eye Proteins
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