Scaling success: Linking public breeding with private enterprise

<p>The known Downstream Promoter Element and Initiator site motifs are shown in boldface.</p

Classification of heterogeneous microarray data by maximum entropy kernel-0

<p><b>Copyright information:</b></p><p>Taken from "Classification of heterogeneous microarray data by maximum entropy kernel"</p><p>http://www.biomedcentral.com/1471-2105/8/267</p><p>BMC Bioinformatics 2007;8():267-267.</p><p>Published online 26 Jul 2007</p><p>PMCID:PMC1994960.</p><p></p>, polynomial, and RBF kernels and the two distance-based kernels for various numbers of feature genes

Classification of heterogeneous microarray data by maximum entropy kernel-2

<p><b>Copyright information:</b></p><p>Taken from "Classification of heterogeneous microarray data by maximum entropy kernel"</p><p>http://www.biomedcentral.com/1471-2105/8/267</p><p>BMC Bioinformatics 2007;8():267-267.</p><p>Published online 26 Jul 2007</p><p>PMCID:PMC1994960.</p><p></p> squamous cell carcinoma of the oral cavity. Classification accuracies of three kernels, i.e., linear, polynomial, RBF, and ME with NND denoising, are compared. Accuracies are measured by (a) predicting each dataset separately and averaged, and (b) predicting the mixed dataset

Classification of heterogeneous microarray data by maximum entropy kernel-1

<p><b>Copyright information:</b></p><p>Taken from "Classification of heterogeneous microarray data by maximum entropy kernel"</p><p>http://www.biomedcentral.com/1471-2105/8/267</p><p>BMC Bioinformatics 2007;8():267-267.</p><p>Published online 26 Jul 2007</p><p>PMCID:PMC1994960.</p><p></p> noise levels, even with SVD denoising applied, while those of ME and other distance-based kernels with NND denoising are sustained at high levels at 10–40% noise level

STAT1 regression results with two filtering methods: Q-value (right) and promoter proximity (left).

<p>The correlation coefficients on the test data between peak values and their predicted values are 0.65 and 0.41 for Q-value and promoter proximity filterings, respectively.</p

List of putative RNA Polymerase II binding motifs identified by PeakRegressor.

<p>The known Downstream Promoter Element and Initiator site motifs are shown in boldface.</p

Schematic view of the workflow of PeakRegressor.

<p>PeakRegressor takes ChIP-Seq data as input and outputs a list of TFBM candidates and their weights that give the best regression accuracies.</p

List of putative RNA Polymerase II binding motifs identified by partial least squares regression.

<p>The known Downstream Promoter Element and Initiator site motifs are shown in boldface.</p

List of putative STAT1 binding motifs identified by principal component regression.

<p>The classical GAS motifs are shown in boldface.</p

List of putative STAT1 binding motifs identified by linear least squares regression.

<p>The classical GAS motifs are shown in boldface.</p