High level flow chart of the <i>wKIERA</i> Algorithm.

<p>High level flow chart of the <i>wKIERA</i> Algorithm.</p

Performance of variable subsets on simulated datasets.

<p>A) LOI dataset (wKIERA settings: <i>poolsize</i> = 10, <i>maxiter</i> = 400, <i>rep</i> = 2000, wkRBF ρ = 0.1); B) NLG dataset (<i>poolsize</i> = 10, <i>maxiter</i> = 400, <i>rep</i> = 2000, wkPoly d = 2). <u>Top</u>: Average SVM accuracy on 100 randomly train/test splits using subsets of variables obtained by thresholding the estimated factors of a weighted kernel with the corresponding cutoff on horizontal axis. Resulting subset size (number of variables) is shown in brackets. <u>Middle</u>: Comparison of classification accuracy of SVM trained using variables selected by best-wKIERA-ranked (red); worst-wKIERA-ranked (black); rank correlation coefficients (blue) and using all variables (green). Results are averaged over 100 randomly training/test splits. <u>Bottom</u>: ROC-space analysis of the SVM classifiers shown in the mid plot.</p

Description of simulated and biological datasets used in this study.

<p>D = dimension, R = number of relevant variables.</p

Weighted Kernel-based Iterative Estimation of Relevance Algorithm (wKIERA).

<p>Weighted Kernel-based Iterative Estimation of Relevance Algorithm (wKIERA).</p

Selected variables in synthetic datasets by wKIERA (<i>poolsize</i> = 10, <i>maxiter</i> = 400).

<p>Type of kernel used in each dataset, weighted RBF kernel (wkRBF) or weighted Polynomial kernel (wkPoly), is showed in rightmost column. Numbers in <b><i>bold-italic</i></b> represent true relevant variables.</p

Performance of variable subsets on proteomic datasets.

<p>A) HAT dataset (wKIERA settings: <i>poolsize</i> = 10, <i>maxiter</i> = 400, <i>rep</i> = 2000, wkRBF ρ = 0.01); B) TB dataset (<i>poolsize</i> = 10, <i>maxiter</i> = 400, <i>rep</i> = 2000, wkRBF ρ = 1). C) MALARIA dataset (<i>poolsize</i> = 10, <i>maxiter</i> = 400, <i>rep</i> = 2000, wkRBF ρ = 1). <u>Top</u>, <u>Middle</u> and <u>Bottom</u>: See legend on <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0001806#pone-0001806-g002" target="_blank">Figure 2</a>.</p

Performance of variable subsets on gene expression microarray datasets.

<p>A) COLON CANCER dataset (wKIERA settings: <i>poolsize</i> = 100, <i>maxiter</i> = 1000, <i>rep</i> = 1000, wkRBF ρ = 0.1); B) GLIAL CANCER dataset (<i>poolsize</i> = 100, <i>maxiter</i> = 1000, <i>rep</i> = 1000, wkRBF ρ = 1×10⁻⁵). <u>Top</u>, <u>Middle</u> and <u>Bottom</u>: See legend on <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0001806#pone-0001806-g002" target="_blank">Figure 2</a>.</p

<i>P</i>. <i>knowlesi</i> clinical isolate genome sequence summary report.

<p>Genome Size 23487363</p><p><i>P</i>. <i>knowlesi</i> clinical isolate genome sequence summary report.</p

The number and position of SNP sites per gene co-associating with the <i>P</i>. <i>knowelsi</i> genome-wide dimorphism.

<p>Non-synonymous polymorphisms (red) are shown above the line and synonymous polymorphisms (blue) are shown below the line. The line is drawn at zero. The chromosomes are drawn to scale and the height of the bars represents the number of SNP sites per gene per region of each chromosome. The scale is given in the boxed area and is the number of SNP sites per gene.</p

A screen shot of Artemis DNA view comparing six <i>Plasmodium knowlesi</i> genome sequences from patient isolates to the <i>Plasmodium knowlesi</i> H strain reference genome sequence.

<p>The <i>P</i>. <i>knowlesi normocyte binding protein xa</i> locus on chromosome 14 is shown. The screen shot shows segregation of the sequences from patient isolates into two groups, (n = 3 in each group) and the dimorphism is clearly visible.</p