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

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

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

Additional file 2 of MVDA: a multi-view genomic data integration methodology

Each sheet refers to each dataset analysed, reporting the results of the single-view clustering patients. Clustering errors for each algorithm and each cut of feature are also reported. (XLSX 54 kb

Additional file 1 of MVDA: a multi-view genomic data integration methodology

It contains a section for each step of the methodology in which the tables and figures with the results for each dataset are reported. (PDF 1495 kb

Reduced inflammatory gene response to silica.

<p>A. Gene expression microarray was performed using lung tissue mRNA isolated from 6 months old mice (n = 4 in each group). The number of upregulated or downregulated genes are indicated. B. Bubble plots for all immune-related annotations. It compares the most significant Gene Ontology (GO) terms from the “Immune-related Biological Process” ontology found across the different experimental conditions. The same selection strategy was applied for all conditions, which was a significance threshold of 0.05 for the adjusted enrichment p-value, at least five genes from the input list in the enriched category and the whole genome as reference background. C. and D. Quantitative RT-PCR analyses of selected genes identified as differentially expressed in the microarray. The results are presented as box blots. The p values were calculated using the Mann-Whitney U-test (at 2 weeks n = 8; at 2 months n = 4). WT = wild type mice; TG = gremlin-1 transgenic mice.</p

Differential cell counts.

<p>Differential cell counts.</p

Top genes differentially regulated in gremlin-1 transgenic lung.

<p>Top genes differentially regulated in gremlin-1 transgenic lung.</p