<?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%">Alonso, Roberto</style></author><author><style face="normal" font="default" size="100%">Salavert, Francisco</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</style></author><author><style face="normal" font="default" size="100%">Bleda, Marta</style></author><author><style face="normal" font="default" size="100%">García-Alonso, Luz</style></author><author><style face="normal" font="default" size="100%">Sanchis-Juan, Alba</style></author><author><style face="normal" font="default" size="100%">Perez-Gil, Daniel</style></author><author><style face="normal" font="default" size="100%">Marin-Garcia, Pablo</style></author><author><style face="normal" font="default" size="100%">Sánchez, Rubén</style></author><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Hidalgo, Marta R</style></author><author><style face="normal" font="default" size="100%">Amadoz, Alicia</style></author><author><style face="normal" font="default" size="100%">Hernansaiz-Ballesteros, Rosa D</style></author><author><style face="normal" font="default" size="100%">Alemán, Alejandro</style></author><author><style face="normal" font="default" size="100%">Tárraga, Joaquín</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Babelomics 5.0: functional interpretation for new generations of genomic data.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic acids research</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">babelomics</style></keyword><keyword><style  face="normal" font="default" size="100%">data integration</style></keyword><keyword><style  face="normal" font="default" size="100%">gene set analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">interactome</style></keyword><keyword><style  face="normal" font="default" size="100%">network analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">NGS</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA-seq</style></keyword><keyword><style  face="normal" font="default" size="100%">Systems biology</style></keyword><keyword><style  face="normal" font="default" size="100%">transcriptomics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015 Apr 20</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://nar.oxfordjournals.org/content/43/W1/W117</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">W1</style></number><volume><style face="normal" font="default" size="100%">43</style></volume><pages><style face="normal" font="default" size="100%">W117-W121</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Babelomics has been running for more than one decade offering a user-friendly interface for the functional analysis of gene expression and genomic data. Here we present its fifth release, which includes support for Next Generation Sequencing data including gene expression (RNA-seq), exome or genome resequencing. Babelomics has simplified its interface, being now more intuitive. Improved visualization options, such as a genome viewer as well as an interactive network viewer, have been implemented. New technical enhancements at both, client and server sides, makes the user experience faster and more dynamic. Babelomics offers user-friendly access to a full range of methods that cover: (i) primary data analysis, (ii) a variety of tests for different experimental designs and (iii) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context. In addition to the public server, local copies of Babelomics can be downloaded and installed. Babelomics is freely available at: http://www.babelomics.org.</style></abstract></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%">Ponzoni, Ignacio</style></author><author><style face="normal" font="default" size="100%">Nueda, María</style></author><author><style face="normal" font="default" size="100%">Tarazona, Sonia</style></author><author><style face="normal" font="default" size="100%">Götz, Stefan</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Dussaut, Julieta</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Conesa, Ana</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pathway network inference from gene expression data.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Syst Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">BMC Syst Biol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Alzheimer Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Cycle</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA Replication</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Regulatory Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Gluconeogenesis</style></keyword><keyword><style  face="normal" font="default" size="100%">Glycolysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Oxidative Phosphorylation</style></keyword><keyword><style  face="normal" font="default" size="100%">Proteolysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Purines</style></keyword><keyword><style  face="normal" font="default" size="100%">Saccharomyces cerevisiae</style></keyword><keyword><style  face="normal" font="default" size="100%">Systems biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Ubiquitin</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8 Suppl 2</style></volume><pages><style face="normal" font="default" size="100%">S7</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;The development of high-throughput omics technologies enabled genome-wide measurements of the activity of cellular elements and provides the analytical resources for the progress of the Systems Biology discipline. Analysis and interpretation of gene expression data has evolved from the gene to the pathway and interaction level, i.e. from the detection of differentially expressed genes, to the establishment of gene interaction networks and the identification of enriched functional categories. Still, the understanding of biological systems requires a further level of analysis that addresses the characterization of the interaction between functional modules.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;We present a novel computational methodology to study the functional interconnections among the molecular elements of a biological system. The PANA approach uses high-throughput genomics measurements and a functional annotation scheme to extract an activity profile from each functional block -or pathway- followed by machine-learning methods to infer the relationships between these functional profiles. The result is a global, interconnected network of pathways that represents the functional cross-talk within the molecular system. We have applied this approach to describe the functional transcriptional connections during the yeast cell cycle and to identify pathways that change their connectivity in a disease condition using an Alzheimer example.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;PANA is a useful tool to deepen in our understanding of the functional interdependences that operate within complex biological systems. We show the approach is algorithmically consistent and the inferred network is well supported by the available functional data. The method allows the dissection of the molecular basis of the functional connections and we describe the different regulatory mechanisms that explain the network's topology obtained for the yeast cell cycle data.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/25032889?dopt=Abstract</style></custom1></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%">Sebastián-Leon, Patricia</style></author><author><style face="normal" font="default" size="100%">Vidal, Enrique</style></author><author><style face="normal" font="default" size="100%">Minguez, Pablo</style></author><author><style face="normal" font="default" size="100%">Ana Conesa</style></author><author><style face="normal" font="default" size="100%">Sonia Tarazona</style></author><author><style face="normal" font="default" size="100%">Amadoz, Alicia</style></author><author><style face="normal" font="default" size="100%">Armero, Carmen</style></author><author><style face="normal" font="default" size="100%">Salavert, Francisco</style></author><author><style face="normal" font="default" size="100%">Vidal-Puig, Antonio</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Joaquín Dopazo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Understanding disease mechanisms with models of signaling pathway activities.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC systems biology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Disease mechanism</style></keyword><keyword><style  face="normal" font="default" size="100%">pathway</style></keyword><keyword><style  face="normal" font="default" size="100%">signalling</style></keyword><keyword><style  face="normal" font="default" size="100%">Systems biology</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014 Oct 25</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.biomedcentral.com/1752-0509/8/121/abstract</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">121</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">BackgroundUnderstanding the aspects of the cell functionality that account for disease or drug action mechanisms is one of the main challenges in the analysis of genomic data and is on the basis of the future implementation of precision medicine.ResultsHere we propose a simple probabilistic model in which signaling pathways are separated into elementary sub-pathways or signal transmission circuits (which ultimately trigger cell functions) and then transforms gene expression measurements into probabilities of activation of such signal transmission circuits. Using this model, differential activation of such circuits between biological conditions can be estimated. Thus, circuit activation statuses can be interpreted as biomarkers that discriminate among the compared conditions. This type of mechanism-based biomarkers accounts for cell functional activities and can easily be associated to disease or drug action mechanisms. The accuracy of the proposed model is demonstrated with simulations and real datasets.ConclusionsThe proposed model provides detailed information that enables the interpretation disease mechanisms as a consequence of the complex combinations of altered gene expression values. Moreover, it offers a framework for suggesting possible ways of therapeutic intervention in a pathologically perturbed system.</style></abstract></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%">Hernández, Pilar</style></author><author><style face="normal" font="default" size="100%">Huerta-Cepas, Jaime</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Al-Shahrour, Fátima</style></author><author><style face="normal" font="default" size="100%">Valls, Joan</style></author><author><style face="normal" font="default" size="100%">Gómez, Laia</style></author><author><style face="normal" font="default" size="100%">Capellà, Gabriel</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Pujana, Miguel Angel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evidence for systems-level molecular mechanisms of tumorigenesis.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Genomics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">BMC Genomics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cell Transformation, Neoplastic</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Neoplastic</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Biological</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Statistical</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasm Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Prostatic Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Interaction Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Messenger</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Systems biology</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2007 Jun 20</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">185</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Cancer arises from the consecutive acquisition of genetic alterations. Increasing evidence suggests that as a consequence of these alterations, molecular interactions are reprogrammed in the context of highly connected and regulated cellular networks. Coordinated reprogramming would allow the cell to acquire the capabilities for malignant growth.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Here, we determine the coordinated function of cancer gene products (i.e., proteins encoded by differentially expressed genes in tumors relative to healthy tissue counterparts, hereafter referred to as &quot;CGPs&quot;) defined as their topological properties and organization in the interactome network. We show that CGPs are central to information exchange and propagation and that they are specifically organized to promote tumorigenesis. Centrality is identified by both local (degree) and global (betweenness and closeness) measures, and systematically appears in down-regulated CGPs. Up-regulated CGPs do not consistently exhibit centrality, but both types of cancer products determine the overall integrity of the network structure. In addition to centrality, down-regulated CGPs show topological association that correlates with common biological processes and pathways involved in tumorigenesis.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;Given the current limited coverage of the human interactome, this study proposes that tumorigenesis takes place in a specific and organized way at the molecular systems-level and suggests a model that comprises the precise down-regulation of groups of topologically-associated proteins involved in particular functions, orchestrated with the up-regulation of specific proteins.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/17584915?dopt=Abstract</style></custom1></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%">Al-Shahrour, Fátima</style></author><author><style face="normal" font="default" size="100%">Arbiza, Leonardo</style></author><author><style face="normal" font="default" size="100%">Dopazo, Hernán</style></author><author><style face="normal" font="default" size="100%">Huerta-Cepas, Jaime</style></author><author><style face="normal" font="default" size="100%">Minguez, Pablo</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">From genes to functional classes in the study of biological systems.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Bioinformatics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">BMC Bioinformatics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Chromosome Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer Simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Biological</style></keyword><keyword><style  face="normal" font="default" size="100%">Multigene Family</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Systems biology</style></keyword><keyword><style  face="normal" font="default" size="100%">User-Computer Interface</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2007 Apr 03</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">114</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;With the popularization of high-throughput techniques, the need for procedures that help in the biological interpretation of results has increased enormously. Recently, new procedures inspired in systems biology criteria have started to be developed.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Here we present FatiScan, a web-based program which implements a threshold-independent test for the functional interpretation of large-scale experiments that does not depend on the pre-selection of genes based on the multiple application of independent tests to each gene. The test implemented aims to directly test the behaviour of blocks of functionally related genes, instead of focusing on single genes. In addition, the test does not depend on the type of the data used for obtaining significance values, and consequently different types of biologically informative terms (gene ontology, pathways, functional motifs, transcription factor binding sites or regulatory sites from CisRed) can be applied to different classes of genome-scale studies. We exemplify its application in microarray gene expression, evolution and interactomics.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;Methods for gene set enrichment which, in addition, are independent from the original data and experimental design constitute a promising alternative for the functional profiling of genome-scale experiments. A web server that performs the test described and other similar ones can be found at: http://www.babelomics.org.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/17407596?dopt=Abstract</style></custom1></record></records></xml>