title: A computational method to differentiate normal individuals, osteoarthritis and rheumatoid arthritis patients using serum biomarkers.
abstract: The objective of this study was to develop a method for categorizing normal individuals (normal, n = 100) as well as patients with osteoarthritis (OA, n = 100), and rheumatoid arthritis (RA, n = 100) based on a panel of inflammatory cytokines expressed in serum samples. Two panels of inflammatory proteins were used as training sets in the construction of two separate artificial neural networks (ANNs). The first training set consisted of all proteins (38 in total) and the second consisted of only the significantly different proteins expressed (12 in total) between at least two patient groups. Both ANNs obtained high levels of sensitivity and specificity, with the first and second ANN each diagnosing 100% of test set patients correctly. These results were then verified by re-investigating the entire dataset using a decision tree algorithm. We show that ANNs can be used for the accurate differentiation between serum samples of patients with OA, a diagnosed RA patient comparator cohort and normal/control cohort. Using neural network and systems biology approaches to manage large datasets derived from high-throughput proteomics should be further explored and considered for diagnosing diseases with complex pathologies.
authors.affiliation: McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, Alberta, Canada Department of Surgery, University of Calgary, Calgary, Alberta, Canada.
authors.forename: Bryan J
authors.initials: BJ
authors.lastname: Heard
authors.affiliation: McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, Alberta, Canada Department of Civil Engineering, Faculty of Engineering, University of Calgary, Calgary, Alberta, Canada.
authors.forename: Joshua M
authors.initials: JM
authors.lastname: Rosvold
authors.affiliation: McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, Alberta, Canada.
authors.forename: Marvin J
authors.initials: MJ
authors.lastname: Fritzler
authors.affiliation: Arthritis Centre, University of Manitoba, Winnipeg, Manitoba, Canada.
authors.forename: Hani
authors.initials: H
authors.lastname: El-Gabalawy
authors.affiliation: Sports Medicine Centre, Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada.
authors.forename: J Preston
authors.initials: JP
authors.lastname: Wiley
authors.affiliation: McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, Alberta, Canada Department of Anatomy and Cell Biology, University of Calgary, Calgary, Alberta, Canada rkrawetz@ucalgary.ca.
authors.forename: Roman J
authors.initials: RJ
authors.lastname: Krawetz
mesh_terms: D000328:Adult; D000465:Algorithms; D001172:Arthritis, Rheumatoid / Q000097:blood* / Q000175:diagnosis*; D015415:Biomarkers / Q000097:blood; D016207:Cytokines / Q000097:blood*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D010003:Osteoarthritis / Q000097:blood* / Q000175:diagnosis*; D010363:Pattern Recognition, Automated / Q000379:methods; D012016:Reference Values; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D015415:Biomarkers; D016207:Cytokines
keywords: artificial neural network; cytokines; diagnostic; machine learning; osteoarthritis; serum