<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fatima Al-Shahrour</style></author><author><style face="normal" font="default" size="100%">Diaz-Uriarte, R.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Discovering molecular functions significantly related to phenotypes by combining gene expression data and biological information</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">babelomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Biological Neoplasm Proteins/genetics/*metabolism Phenotype Software Structure-Activity Relationship Systems Integration Tumor Markers</style></keyword><keyword><style  face="normal" font="default" size="100%">Biological/genetics/*metabolism</style></keyword><keyword><style  face="normal" font="default" size="100%">Breast Neoplasms/genetics/*metabolism Computer Simulation *Database Management Systems *Databases</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Documentation/methods Gene Expression Profiling/*methods Humans *Models</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=15840702</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">13</style></number><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">2988-93</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;MOTIVATION: The analysis of genome-scale data from different high throughput techniques can be used to obtain lists of genes ordered according to their different behaviours under distinct experimental conditions corresponding to different phenotypes (e.g. differential gene expression between diseased samples and controls, different response to a drug, etc.). The order in which the genes appear in the list is a consequence of the biological roles that the genes play within the cell, which account, at molecular scale, for the macroscopic differences observed between the phenotypes studied. Typically, two steps are followed for understanding the biological processes that differentiate phenotypes at molecular level: first, genes with significant differential expression are selected on the basis of their experimental values and subsequently, the functional properties of these genes are analysed. Instead, we present a simple procedure which combines experimental measurements with available biological information in a way that genes are simultaneously tested in groups related by common functional properties. The method proposed constitutes a very sensitive tool for selecting genes with significant differential behaviour in the experimental conditions tested. RESULTS: We propose the use of a method to scan ordered lists of genes. The method allows the understanding of the biological processes operating at molecular level behind the macroscopic experiment from which the list was generated. This procedure can be useful in situations where it is not possible to obtain statistically significant differences based on the experimental measurements (e.g. low prevalence diseases, etc.). Two examples demonstrate its application in two microarray experiments and the type of information that can be extracted.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Al-Shahrour, Fatima Diaz-Uriarte, Ramon Dopazo, Joaquin Evaluation Studies Research Support, Non-U.S. Gov’t England Bioinformatics (Oxford, England) Bioinformatics. 2005 Jul 1;21(13):2988-93. Epub 2005 Apr 19.&lt;/p&gt;</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fatima Al-Shahrour</style></author><author><style face="normal" font="default" size="100%">Diaz-Uriarte, R.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">*Algorithms Artificial Intelligence Databases</style></keyword><keyword><style  face="normal" font="default" size="100%">babelomics</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA/*methods *Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Gene Expression Profiling/*methods *Hypermedia Information Storage and Retrieval/*methods *Internet *Phylogeny Sequence Alignment/methods Sequence Analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://bioinformatics.oxfordjournals.org/content/20/4/578.abstract</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">578-80</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We present a simple but powerful procedure to extract Gene Ontology (GO) terms that are significantly over- or under-represented in sets of genes within the context of a genome-scale experiment (DNA microarray, proteomics, etc.). Said procedure has been implemented as a web application, FatiGO, allowing for easy and interactive querying. FatiGO, which takes the multiple-testing nature of statistical contrast into account, currently includes GO associations for diverse organisms (human, mouse, fly, worm and yeast) and the TrEMBL/Swissprot GOAnnotations@EBI correspondences from the European Bioinformatics Institute.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Al-Shahrour, Fatima Diaz-Uriarte, Ramon Dopazo, Joaquin England Bioinformatics (Oxford, England) Bioinformatics. 2004 Mar 1;20(4):578-80. Epub 2004 Jan 22.&lt;/p&gt;</style></notes></record></records></xml>