Organization of the Autonomic Nuclei in the Spinal Cord : Functional Morphology

Disease-associated genes. Complete list of the disease-associated genes for each dataset. (XZ 46 kb

Additional file 1 of RGIFE: a ranked guided iterative feature elimination heuristic for the identification of biomarkers

Supplementary material. Supporting information referenced in the main text. (PDF 464 kb

Leave-one-out cross-validation results.

<p>Leave-one-out cross-validation results obtained with BioHEL, SVM, RF and PAM on the three microarray datasets using three feature selection methods (CFS, PLSS, RFS); AVG  =  average accuracy, STDDEV  =  standard deviation; the highest accuracies achieved with BioHEL and the best alternative are both shown in bold type for each dataset.</p

List of high scoring genes for the prostate cancer dataset.

<p>List of genes that were chosen by at least two different selection methods among the 20 features selected most frequently on the prostate dataset. The 4 genes detected as informative by both the Ensembl FS and the BioHEL FR approach (<i>hepsin</i>, <i>nel-like 2</i>, <i>AMACR</i> and <i>adipsin</i>) are highlighted in bold face (see discussion in the literature mining analysis section).</p

Flowchart illustrating the experimental procedure.

<p>The protocol consists of three steps: 1) Pre-processing; 2) Supervised analysis; 3) Post-analysis.</p

Comparison of text mining scores.

<p>Histogram of text mining scores for randomly chosen gene identifier subsets compared to scores achieved by BioHEL and the ensemble feature selection (FS) approach (lymphoma cancer dataset).</p

List of high scoring genes for the breast cancer dataset.

<p>List of genes that were chosen by at least two different selection methods among the 30 features selected most frequently on the breast cancer dataset. The 7 genes detected as informative by both the Ensembl FS and the BioHEL FR approach are highlighted in bold face (see discussion in the literature mining analysis section).</p

Comparison of feature selection methods.

<p>Results of a Friedman test to compare feature selection methods in terms of classification accuracy across different datasets and prediction methods (the best average ranks for each row are shown in bold typeface).</p

Comparison of prediction results from the literature for the prostate cancer dataset.

<p>(*maximum no. of genes per base classifier in ensemble learning model; **evaluation results averaged over feature subsets using different numbers of genes; ***singular value decomposition used instead of classical feature selection).</p

List of high scoring genes for the lymphoma dataset.

<p>List of genes that were chosen by at least two different selection methods among the 30 features selected most frequently on the lymphoma dataset. On this dataset, the genes detected as informative by the Ensembl FS and the BioHEL FR did not overlap (see discussion in the literature mining analysis section).</p