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. 2017 Jul 26;7:6548. doi: 10.1038/s41598-017-06738-3

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© The Author(s) 2017

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Comparison of Predictive Performance for Gene Expression and Clinical Biomarkers. (A) LASSO-penalized logistic regression retained 3 pathways consisting of a total of 11 genes providing the greatest predictive value for classifying subjects as bacterial or non-bacterial. *LASSO Pathway OR are odds ratios per SD of the hard-thresholded 1st PC of the nominally significant genes within the pathway. ***Constrained Gene OR = exp (Gene Loading * log (LASSO Pathway OR)/SDPathway) = (LASSO Pathway OR)(Gene Loading/(SD of Pathway)). (B) Cross-validation chose a 4-predictor model consisting of nasal congestion, infiltrates on chest radiograph, blood urea nitrogen levels and white blood cell count. (C) Area Under the ROC Curve (AUC) characteristics are shown for fully nested cross-validated estimates using the “pathway”-selected 11 gene set, the “array”-selected 10 gene set, and the 4 clinical variables. These data indicate that our pathway-based 11-gene predictor outperforms both the clinical and array-based gene models.