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J. Clin. Med. 2019, 8(1), 50; https://doi.org/10.3390/jcm8010050

Efficacy of Integrating a Novel 16-Gene Biomarker Panel and Intelligence Classifiers for Differential Diagnosis of Rheumatoid Arthritis and Osteoarthritis

1
College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Korea
2
Department of Statistics, Seoul National University, Seoul 08826, Korea
3
School of Medicine, Vietnam National University, Ho Chi Minh 700000, Vietnam
*
Author to whom correspondence should be addressed.
Received: 19 November 2018 / Revised: 20 December 2018 / Accepted: 2 January 2019 / Published: 6 January 2019
(This article belongs to the Special Issue The Future of Artificial Intelligence in Clinical Medicine)
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Abstract

Introducing novel biomarkers for accurately detecting and differentiating rheumatoid arthritis (RA) and osteoarthritis (OA) using clinical samples is essential. In the current study, we searched for a novel data-driven gene signature of synovial tissues to differentiate RA from OA patients. Fifty-three RA, 41 OA, and 25 normal microarray-based transcriptome samples were utilized. The area under the curve random forests (RF) variable importance measurement was applied to seek the most influential differential genes between RA and OA. Five algorithms including RF, k-nearest neighbors (kNN), support vector machines (SVM), naïve-Bayes, and a tree-based method were employed for the classification. We found a 16-gene signature that could effectively differentiate RA from OA, including TMOD1, POP7, SGCA, KLRD1, ALOX5, RAB22A, ANK3, PTPN3, GZMK, CLU, GZMB, FBXL7, TNFRSF4, IL32, MXRA7, and CD8A. The externally validated accuracy of the RF model was 0.96 (sensitivity = 1.00, specificity = 0.90). Likewise, the accuracy of kNN, SVM, naïve-Bayes, and decision tree was 0.96, 0.96, 0.96, and 0.91, respectively. Functional meta-analysis exhibited the differential pathological processes of RA and OA; suggested promising targets for further mechanistic and therapeutic studies. In conclusion, the proposed genetic signature combined with sophisticated classification methods may improve the diagnosis and management of RA patients. View Full-Text
Keywords: rheumatoid arthritis; osteoarthritis; diagnostic biomarker; machine learning; meta-analysis; pathway analysis rheumatoid arthritis; osteoarthritis; diagnostic biomarker; machine learning; meta-analysis; pathway analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Long, N.P.; Park, S.; Anh, N.H.; Min, J.E.; Yoon, S.J.; Kim, H.M.; Nghi, T.D.; Lim, D.K.; Park, J.H.; Lim, J.; Kwon, S.W. Efficacy of Integrating a Novel 16-Gene Biomarker Panel and Intelligence Classifiers for Differential Diagnosis of Rheumatoid Arthritis and Osteoarthritis. J. Clin. Med. 2019, 8, 50.

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