Rank differences and <i>p</i>-values for pair-wise comparison of encodings.

<p>For each pair-wise comparison of encodings, the rank difference was calculated as the difference of the average ranks over all data sets and algorithms. The first encoding of each hypothesis is the better one (lower rank). <i>p</i>-values were corrected using Shaffer’s static method.</p><p>Rank differences and <i>p</i>-values for pair-wise comparison of encodings.</p

Average ranks of the seven classification algorithms.

<p>The average ranks of the Friedman test for the seven different classifiers using the additive encoding. (Small values are better.) The result of the Friedman test over all data sets is significant (<i>p</i> < 10⁻¹⁵ for <i>k</i> = 7, <i>n</i> = 42). The table also shows the average ranks for each data set separately, but the Friedman test is not applicable here because the number of treatments is bigger than the number of problems (<i>k</i> = 7, <i>n</i> = 6).</p><p>Average ranks of the seven classification algorithms.</p

Comparison of classification algorithms.

<p>The seven classification algorithms compared by their rank distance over all disease data sets using the additive encoding. A connecting line between encodings means that the null hypothesis of them being significantly different could not be rejected (with <i>α</i> = 0.001).</p

Average AUC for each data set and algorithm over all <i>p</i>-value thresholds.

<p>All values are AUCs averaged over all <i>p</i>-value thresholds for each data set and algorithm. The last row shows the average AUC for each data set over all <i>p</i>-value thresholds and algorithms.</p><p>Average AUC for each data set and algorithm over all <i>p</i>-value thresholds.</p

Average number of SNPs reaching the specified <i>p</i>-value threshold per data set.

<p>Average number of SNPs reaching the specified <i>p</i>-value thresholds for at least one of the tests for genome-wide association. Numbers are averaged over all 10 results of the 5 × 2 cross-validations and rounded to one decimal place.</p><p>Average number of SNPs reaching the specified <i>p</i>-value threshold per data set.</p

Comparison of encodings per disease data set.

<p>The three encodings compared by their rank distance over all data sets and classifiers (a) and grouped by disease data set. A connecting line between encodings means that the null hypothesis of them being significantly different could not be rejected. Only data sets for which the Friedman test rejected the null hypothesis are shown. (<i>α</i> = 0.001.)</p

Rank differences and <i>p</i>-values for pair-wise comparison of classification algorithms.

<p>For each pair-wise comparison of classification algorithms, the rank difference was calculated as the difference of the average ranks over all data sets. The first algorithm of each hypothesis is the better one (lower rank). <i>p</i>-values were corrected using Shaffer’s static method.</p><p>Rank differences and <i>p</i>-values for pair-wise comparison of classification algorithms.</p

ZBIT Bioinformatics Toolbox: A Web-Platform for Systems Biology and Expression Data Analysis

<div><p>Bioinformatics analysis has become an integral part of research in biology. However, installation and use of scientific software can be difficult and often requires technical expert knowledge. Reasons are dependencies on certain operating systems or required third-party libraries, missing graphical user interfaces and documentation, or nonstandard input and output formats. In order to make bioinformatics software easily accessible to researchers, we here present a web-based platform. The Center for Bioinformatics Tuebingen (ZBIT) Bioinformatics Toolbox provides web-based access to a collection of bioinformatics tools developed for systems biology, protein sequence annotation, and expression data analysis. Currently, the collection encompasses software for conversion and processing of community standards SBML and BioPAX, transcription factor analysis, and analysis of microarray data from transcriptomics and proteomics studies. All tools are hosted on a customized Galaxy instance and run on a dedicated computation cluster. Users only need a web browser and an active internet connection in order to benefit from this service. The web platform is designed to facilitate the usage of the bioinformatics tools for researchers without advanced technical background. Users can combine tools for complex analyses or use predefined, customizable workflows. All results are stored persistently and reproducible. For each tool, we provide documentation, tutorials, and example data to maximize usability. The ZBIT Bioinformatics Toolbox is freely available at <a href="https://webservices.cs.uni-tuebingen.de/" target="_blank">https://webservices.cs.uni-tuebingen.de/</a>.</p></div

TFpredict and SABINE: Sequence-Based Prediction of Structural and Functional Characteristics of Transcription Factors

<div><p>One of the key mechanisms of transcriptional control are the specific connections between transcription factors (TF) and <i>cis</i>-regulatory elements in gene promoters. The elucidation of these specific protein-DNA interactions is crucial to gain insights into the complex regulatory mechanisms and networks underlying the adaptation of organisms to dynamically changing environmental conditions. As experimental techniques for determining TF binding sites are expensive and mostly performed for selected TFs only, accurate computational approaches are needed to analyze transcriptional regulation in eukaryotes on a genome-wide level. We implemented a four-step classification workflow which for a given protein sequence (1) discriminates TFs from other proteins, (2) determines the structural superclass of TFs, (3) identifies the DNA-binding domains of TFs and (4) predicts their <i>cis</i>-acting DNA motif. While existing tools were extended and adapted for performing the latter two prediction steps, the first two steps are based on a novel numeric sequence representation which allows for combining existing knowledge from a BLAST scan with robust machine learning-based classification. By evaluation on a set of experimentally confirmed TFs and non-TFs, we demonstrate that our new protein sequence representation facilitates more reliable identification and structural classification of TFs than previously proposed sequence-derived features. The algorithms underlying our proposed methodology are implemented in the two complementary tools TFpredict and SABINE. The online and stand-alone versions of TFpredict and SABINE are freely available to academics at <a href="http://www.cogsys.cs.uni-tuebingen.de/software/TFpredict/" target="_blank">http://www.cogsys.cs.uni-tuebingen.de/software/TFpredict/</a> and <a href="http://www.cogsys.cs.uni-tuebingen.de/software/SABINE/" target="_blank">http://www.cogsys.cs.uni-tuebingen.de/software/SABINE/</a>.</p></div

EDISA: extracting biclusters from multiple time-series of gene expression profiles-1

<p><b>Copyright information:</b></p><p>Taken from "EDISA: extracting biclusters from multiple time-series of gene expression profiles"</p><p>http://www.biomedcentral.com/1471-2105/8/334</p><p>BMC Bioinformatics 2007;8():334-334.</p><p>Published online 12 Sep 2007</p><p>PMCID:PMC2063505.</p><p></p>s of noise. The overlap of the implanted modules and the modules mined by EDISA were scored (equation 15). Six runs with 400 iterations were performed, with = 0.1 and = 0.2 for ∈ [0,0.5], = 0.15 for = 0.7 and = 0.2 for = 0.9