Shiraz E-Medical Journal

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Penalized Regression Versus Random Forest Model in Analyzing High Dimensional Proteomic Data: Diagnosis of IgA Nephropathy

Afshin Almasi 1 , Shiva Kalantari 2 , Amirhossein Hashemian 1 , 3 and Tahereh Mohammadi Majd 1 , *
Authors Information
1 Department of Biostatistics and Epidemiology, School of Public Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
2 Chronic Kidney Disease Research Center, Labbafinejad Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
3 Research Center for Environmental Determinants of Health (RCEDH), Kermanshah University of Medical Sciences, Kermanshah, Iran
Article information
  • Shiraz E-Medical Journal: January 2018, 19 (1); e14931
  • Published Online: September 17, 2017
  • Article Type: Research Article
  • Received: June 10, 2017
  • Revised: June 16, 2017
  • Accepted: September 5, 2017
  • DOI: 10.5812/semj.14931

To Cite: Almasi A, Kalantari S, Hashemian A, Mohammadi Majd T. Penalized Regression Versus Random Forest Model in Analyzing High Dimensional Proteomic Data: Diagnosis of IgA Nephropathy, Shiraz E-Med J. 2018 ; 19(1):e14931. doi: 10.5812/semj.14931.

Abstract
Copyright © 2017, Shiraz E-Medical Journal. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.
1. Background
2. Methods
3. Results
4. Discussion
Acknowledgements
Footnotes
References
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