<?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%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Panadero, Joaquin</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Integrated gene set analysis for microRNA studies.</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Bioinformatics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene ontology</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Regulatory Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">High-Throughput Nucleotide Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">MicroRNAs</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Reproducibility of Results</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016 09 15</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">32</style></volume><pages><style face="normal" font="default" size="100%">2809-16</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;MOTIVATION: &lt;/b&gt;Functional interpretation of miRNA expression data is currently done in a three step procedure: select differentially expressed miRNAs, find their target genes, and carry out gene set overrepresentation analysis Nevertheless, major limitations of this approach have already been described at the gene level, while some newer arise in the miRNA scenario.Here, we propose an enhanced methodology that builds on the well-established gene set analysis paradigm. Evidence for differential expression at the miRNA level is transferred to a gene differential inhibition score which is easily interpretable in terms of gene sets or pathways. Such transferred indexes account for the additive effect of several miRNAs targeting the same gene, and also incorporate cancellation effects between cases and controls. Together, these two desirable characteristics allow for more accurate modeling of regulatory processes.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;We analyze high-throughput sequencing data from 20 different cancer types and provide exhaustive reports of gene and Gene Ontology-term deregulation by miRNA action.&lt;/p&gt;&lt;p&gt;&lt;b&gt;AVAILABILITY AND IMPLEMENTATION: &lt;/b&gt;The proposed methodology was implemented in the Bioconductor library mdgsa http://bioconductor.org/packages/mdgsa For the purpose of reproducibility all of the scripts are available at https://github.com/dmontaner-papers/gsa4mirna&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONTACT: &lt;/b&gt;: david.montaner@gmail.com&lt;/p&gt;&lt;p&gt;&lt;b&gt;SUPPLEMENTARY INFORMATION: &lt;/b&gt;Supplementary data are available at Bioinformatics online.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">18</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/27324197?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%">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%">Galan, Amparo</style></author><author><style face="normal" font="default" size="100%">Diaz-Gimeno, Patricia</style></author><author><style face="normal" font="default" size="100%">Poo, Maria Eugenia</style></author><author><style face="normal" font="default" size="100%">Valbuena, Diana</style></author><author><style face="normal" font="default" size="100%">Sanchez, Eva</style></author><author><style face="normal" font="default" size="100%">Ruiz, Veronica</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Conesa, Ana</style></author><author><style face="normal" font="default" size="100%">Simon, Carlos</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Defining the genomic signature of totipotency and pluripotency during early human development.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS One</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS One</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Blastocyst Inner Cell Mass</style></keyword><keyword><style  face="normal" font="default" size="100%">Blastomeres</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Differentiation</style></keyword><keyword><style  face="normal" font="default" size="100%">Embryonic Development</style></keyword><keyword><style  face="normal" font="default" size="100%">Embryonic Stem Cells</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%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Sequence Annotation</style></keyword><keyword><style  face="normal" font="default" size="100%">Pluripotent Stem Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Totipotent Stem Cells</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">e62135</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The genetic mechanisms governing human pre-implantation embryo development and the in vitro counterparts, human embryonic stem cells (hESCs), still remain incomplete. Previous global genome studies demonstrated that totipotent blastomeres from day-3 human embryos and pluripotent inner cell masses (ICMs) from blastocysts, display unique and differing transcriptomes. Nevertheless, comparative gene expression analysis has revealed that no significant differences exist between hESCs derived from blastomeres versus those obtained from ICMs, suggesting that pluripotent hESCs involve a new developmental progression. To understand early human stages evolution, we developed an undifferentiation network signature (UNS) and applied it to a differential gene expression profile between single blastomeres from day-3 embryos, ICMs and hESCs. This allowed us to establish a unique signature composed of highly interconnected genes characteristic of totipotency (61 genes), in vivo pluripotency (20 genes), and in vitro pluripotency (107 genes), and which are also proprietary according to functional analysis. This systems biology approach has led to an improved understanding of the molecular and signaling processes governing human pre-implantation embryo development, as well as enabling us to comprehend how hESCs might adapt to in vitro culture conditions.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/23614026?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%">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%">Multidimensional gene set analysis of genomic data.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS One</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS One</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Databases, Genetic</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%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Statistical</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010 Apr 27</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">e10348</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Understanding the functional implications of changes in gene expression, mutations, etc., is the aim of most genomic experiments. To achieve this, several functional profiling methods have been proposed. Such methods study the behaviour of different gene modules (e.g. gene ontology terms) in response to one particular variable (e.g. differential gene expression). In spite to the wealth of information provided by functional profiling methods, a common limitation to all of them is their inherent unidimensional nature. In order to overcome this restriction we present a multidimensional logistic model that allows studying the relationship of gene modules with different genome-scale measurements (e.g. differential expression, genotyping association, methylation, copy number alterations, heterozygosity, etc.) simultaneously. Moreover, the relationship of such functional modules with the interactions among the variables can also be studied, which produces novel results impossible to be derived from the conventional unidimensional functional profiling methods. We report sound results of gene sets associations that remained undetected by the conventional one-dimensional gene set analysis in several examples. Our findings demonstrate the potential of the proposed approach for the discovery of new cell functionalities with complex dependences on more than one variable.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/20436964?dopt=Abstract</style></custom1></record></records></xml>