Design of Rheumatoid Arthritis Predictor Model Using Machine Learning Algorithms-Reference-Cited by-同舟云学术

Design of Rheumatoid Arthritis Predictor Model Using Machine Learning Algorithms

Published:2017-12-23 Issue: Volume: Page:67-77
ISSN:2191-530X
Container-title:Cognitive Science and Artificial Intelligence
language:
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Author:

Shanmugam S.,Preethi J.

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-10-6698-6_7

Reference15 articles.

1. Mohan, Vasanth Konda, Ganesan, Nalini, and Rajasekhar, Gopalakrishnan. 2014. Association of Susceptible Genetic Markers and Autoantibodies in Rheumatoid Arthritis. Journal of Genetics 93(2): 597–605.

2. Viatte, Sebastien, Darren Plant, and Soumya Raychaudhuri. 2013. Genetics and Epigenetics of Rheumatoid Arthritis. Nature Reviews Rheumatology 9: 141–153. https://doi.org/10.1038/nrrheum.2012.237 . published online 5 February 2013.

3. Bridges Jr., S. Louis, and Robert P. Kimberly. 2002. Genetic Influences on Treatment Response in Rheumatoid Arthritis. In Modern Therapeutics in Rheumatic Diseases, ed. G.C. Tsokos, et al. Totowa, NJ: Humana Press Inc.

4. Soroka, N., et al. 2012. Genetic Markers of Sulphasalazine Adverse Reactions in Rheumatoid Arthritis Patients. Annals of the Rheumatic Diseases 71 (Suppl3): 68.

5. Oliver, James, Darren Plant, Amy P. Webster, and Anne Barton. 2015. Genetic and Genomic Markers of Anti-TNF Treatment Response in Rheumatoid Arthritis. Biomarkers in Medicine 9 (6): 499–512.

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