Discriminative topological features reveal biological network mechanisms-1

<p><b>Copyright information:</b></p><p>Taken from "Discriminative topological features reveal biological network mechanisms"</p><p>BMC Bioinformatics 2004;5():181-181.</p><p>Published online 22 Nov 2004</p><p>PMCID:PMC535926.</p><p>Copyright © 2004 Middendorf et al; licensee BioMed Central Ltd.</p>rapivsky-Bianconi [18, 14] model. is robustly classified as a Kumar network. The Krapivsky-Bianconi model is the runner-up. We here show data for a word that especially the Kumar model over the Krapivsky-Bianconi model. The histograms of the word over the training data are shown along with their associated densities calculated from the data by Gaussian kernel density estimation. The densities give the following log--values at the word value for the network: log() = -4.22, log() = -12.0

Discriminative topological features reveal biological network mechanisms-0

<p><b>Copyright information:</b></p><p>Taken from "Discriminative topological features reveal biological network mechanisms"</p><p>BMC Bioinformatics 2004;5():181-181.</p><p>Published online 22 Nov 2004</p><p>PMCID:PMC535926.</p><p>Copyright © 2004 Middendorf et al; licensee BioMed Central Ltd.</p>and the Grindrod [17] model. is robustly classified as a Middendorf-Ziv network. The Grindrod model is the runner-up. We here show data for a word that especially the Middendorf-Ziv model over the Grindrod model. The histograms of the word over the training data are shown along with their associated densities calculated from the data by Gaussian kernel density estimation. The densities give the following log--values at the word value for the network: log() = -376, log() = -6.23