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ISSN: 0974-276X

Journal of Proteomics & Bioinformatics
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Research Article

Diagnostics for Statistical Variable Selection Methods for Prediction of Peptic Ulcer Disease in Helicobacter pylori Infection

Hyunsu Ju1,3*, Allan R Brasier2,3*, Alexander Kurosky4, Bo Xu5, Victor E Reyes6,7 and David Y Graham4,8

1Departments of Preventive Medicine and Community Health, University of Texas Medical Branch (UTMB), Galveston, TX, USA

2Sealy Center for Molecular Medicine, University of Texas Medical Branch, Galveston, TX, USA

3Institute for Translational Sciences, University of Texas Medical Branch, Galveston, TX, USA

4Departments of Medicine, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX 77030, USA

5Departments of Biochemistry and Molecular Biology, University of Texas Medical Branch, Galveston, TX, USA

6Department of Pediatrics, University of Texas Medical Branch, Galveston, TX, USA

7Department of Microbiology & Immunology, University of Texas Medical Branch, Galveston, TX, USA

8Baylor College of Medicine, Houston TX, USA

*Corresponding Author:
Hyunsu Ju
Departments of Preventive Medicine and Community Health
University of Texas Medical Branch (UTMB)
Galveston, TX, USA
Tel: (409) 772-9144
Fax: (409) 772-9127
E-mail: [email protected]

Allan R Brasier
Sealy Center for Molecular Medicine
University of Texas Medical Branch
Galveston, TX, USA
E-mail: [email protected]

Received Date: January 31, 2014; Accepted Date: March 24, 2014; Published Date: March 28, 2014

Citation: Ju H, Brasier AR, Kurosky A, Xu B, Reyes VE, et al. (2014) Diagnostics for Statistical Variable Selection Methods for Prediction of Peptic Ulcer Disease in Helicobacter pylori Infection. J Proteomics Bioinform 7:095-101. doi:10.4172/jpb.1000308

Copyright: © 2014 Ju H, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Background: The development of accurate classification models depends upon the methods used to identify the most relevant variables. The aim of this article is to evaluate variable selection methods to identify important variables in predicting a binary response using nonlinear statistical models. Our goals in model selection include producing non-overfitting stable models that are interpretable, that generate accurate predictions and have minimum bias. This work was motivated by data on clinical and laboratory features of Helicobacter pylori infections obtained from 60 individuals enrolled in a prospective observational study. Results: We carried out a comprehensive performance comparison of several nonlinear classification models over the H. pylori data set. We compared variable selection results by Multivariate Adaptive Regression Splines (MARS), Logistic Regression with regularization, Generalized Additive Models (GAMs) and Bayesian Variable Selection in GAMs. We found that the MARS model approach has the highest predictive power because the nonlinearity assumptions of candidate predictors are strongly satisfied, a finding demonstrated via deviance chisquare testing procedures in GAMs. Conclusions: Our results suggest that the physiological free amino acids citrulline, histidine, lysine and arginine are the major features for predicting H. pylori peptic ulcer disease on the basis of amino acid profiling.

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