Overview of the analyzed datasets.

<p>For each dataset, the number of samples, the number of features/genes after pre-processing the data, the number of classes and samples specified for each class are reported.</p><p>Overview of the analyzed datasets.</p

Evaluation process.

<p>The full dataset is a matrix with thousands of features (<i>e.g.</i> genes) in rows and tens or hundreds of samples (belonging to different classes) in columns. For each sample, the outcome (class) is given. The dataset is randomly divided into training and test sets using a stratified random selection (1). Within the training set, relevant features are selected using the compared methods (2). The FPRF method identifies a wide set of relevant features using a fuzzy pattern discovery technique and ranks them applying a RF-based procedure (3). The most n-relevant features are then selected with n = 30, 50, 100, 150 and 200 (4). The different sets of features are used to evaluate the stability and the corresponding classification performance. For each set of selected features an RF-based classifier is trained on the training set (5). After training, the classifiers are asked to predict the outcome of the test set patients (6). The predicted outcome is compared with the true outcome and the number of correctly classified samples is noted. Steps 1–6 are repeated 30 times, and the resulting evaluation metrics are obtained by averaging over the 30 runs.</p

Selection consistency analysis.

<p>The number of significantly self-consistent and all the selected genes by a given method during the 30 bootstrap iterations. <i>ns</i> – the number of significantly self-consistent genes found, <i>tot</i> – the number of different features selected over the 30 bootstrap iterations, mnsf – the mean number of selected features. The highest values are highlighted in bold.</p><p>Selection consistency analysis.</p

Running time.

<p>Evaluation of the running time represented as the mean over 30 bootstrap iterations. All methods investigated in this study were run single-threaded. For the proposed method the running time is compiled considering the sum of the execution times spent for the feature selection and prioritization steps.</p><p>Running time.</p

Classification performance.

<p>The mean accuracy values obtained over the 30 bootstrap iterations. Acc – is the overall accuracy, F – is the F-score, G – is the G-score. The highest values are highlighted in bold. NOTE: all the corresponding standard deviations are less than 0.02.</p><p>Classification performance.</p

Level of Fatty Acid Binding Protein 5 (FABP5) Is Increased in Sputum of Allergic Asthmatics and Links to Airway Remodeling and Inflammation

<div><p>Background</p><p>The inflammatory processes in the upper and lower airways in allergic rhinitis and asthma are similar. Induced sputum and nasal lavage fluid provide a non-invasive way to examine proteins involved in airway inflammation in these conditions.</p><p>Objectives</p><p>We conducted proteomic analyses of sputum and nasal lavage fluid samples to reveal differences in protein abundances and compositions between the asthma and rhinitis patients and to investigate potential underlying mechanisms.</p><p>Methods</p><p>Induced sputum and nasal lavage fluid samples were collected from 172 subjects with 1) allergic rhinitis, 2) asthma combined with allergic rhinitis, 3) nonallergic rhinitis and 4) healthy controls. Proteome changes in 21 sputum samples were analysed with two-dimensional difference gel electrophoresis (2D-DIGE), and the found differentially regulated proteins identified with mass spectrometry. Immunological validation of identified proteins in the sputum and nasal lavage fluid samples was performed with Western blot and ELISA.</p><p>Results</p><p>Altogether 31 different proteins were identified in the sputum proteome analysis, most of these were found also in the nasal lavage fluid. Fatty acid binding protein 5 (FABP5) was up-regulated in the sputum of asthmatics. Immunological validation in the whole study population confirmed the higher abundance levels of FABP5 in asthmatic subjects in both the sputum and nasal lavage fluid samples. In addition, the vascular endothelial growth factor (VEGF) level was increased in the nasal lavage fluid of asthmatics and there were positive correlations between FABP5 and VEGF levels (r=0.660, p<0.001) and concentrations of FABP5 and cysteinyl leukotriene (CysLT) (r=0.535, p<0.001) in the nasal lavage fluid.</p><p>Conclusions</p><p>FABP5 may contribute to the airway remodeling and inflammation in asthma by fine-tuning the levels of CysLTs, which induce VEGF production.</p></div

False colour two-dimensional differential gel electrophoresis (2D-DIGE) image of identified protein spots in sputum.

<p>False colour image of the identified protein spots (circled and named) on a 2-D DIGE-gel of the sputum fluid phase samples of 21 subjects subdivided as follows: asthma with allergic rhinitis, allergic rhinitis, nonallergic rhinitis, and healthy controls. Samples of the subjects with asthma with allergic rhinitis (reddish up-regulated) and with nonallergic rhinitis (green up-regulated), as well as the internal standard are shown. The identified proteins are listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127003#pone.0127003.s005" target="_blank">S1 Table</a>.</p

Immunological confirmation of findings.

<p>2D-DIGE gel spot abundances for the FABP5 of the 21 induced sputum samples (A). Western blot analysis for 138 induced sputum samples (B) and for 158 nasal lavage fluid samples (C) with FABP5 antibody. ELISA results for vascular endothelial growth factor (VEGF) (D) and cysteinyl leukotriene (CysLT) (E) measurements from 90 nasal lavage fluid samples. The figures show mean with standard error of the mean. * p < 0.05, **p < 0.01. AR = allergic rhinitis, Asthma+AR = asthma and allergic rhinitis, NAR = nonallergic rhinitis.</p

Heatmap of up- and down-regulated protein gel spots in sputum (A) and in nasal lavage fluid (B).

<p>Hierarchical clustering reveals the differences between the protein regulation of the groups, indicating dissimilarities between asthma (Asthma+AR), allergic rhinitis (AR) and healthy controls, whereas the nonallergic rhinitis (NAR) group shows the least differences to the up- or down-regulation of the healthy controls.</p

Characteristics of study subjects.

<p>*Patients with a negative control wheal of ≥ 2mm were excluded from skin prick test (SPT) analysis n = 166. IgE, immunoglobulin E; FEV1, forced expiratory volume in one second; FVC, forced vital capacity, FeNO, exhaled nitric oxide</p><p>Characteristics of study subjects.</p