<?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%">Garrido-Rodriguez, Martín</style></author><author><style face="normal" font="default" size="100%">López-López, Daniel</style></author><author><style face="normal" font="default" size="100%">Ortuno, Francisco M</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Muñoz, Eduardo</style></author><author><style face="normal" font="default" size="100%">Calzado, Marco A</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%">A versatile workflow to integrate RNA-seq genomic and transcriptomic data into mechanistic models of signaling pathways.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS Comput Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS Comput Biol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Line, Tumor</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Factual</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</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%">Models, Theoretical</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA-seq</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%">Transcriptome</style></keyword><keyword><style  face="normal" font="default" size="100%">whole exome sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Workflow</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 02</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">17</style></volume><pages><style face="normal" font="default" size="100%">e1008748</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;MIGNON is a workflow for the analysis of RNA-Seq experiments, which not only efficiently manages the estimation of gene expression levels from raw sequencing reads, but also calls genomic variants present in the transcripts analyzed. Moreover, this is the first workflow that provides a framework for the integration of transcriptomic and genomic data based on a mechanistic model of signaling pathway activities that allows a detailed biological interpretation of the results, including a comprehensive functional profiling of cell activity. MIGNON covers the whole process, from reads to signaling circuit activity estimations, using state-of-the-art tools, it is easy to use and it is deployable in different computational environments, allowing an optimized use of the resources available.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33571195?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%">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%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Carbonell-Caballero, José</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%">Models of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome.</style></title><secondary-title><style face="normal" font="default" size="100%">Biol Direct</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Biol Direct</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 Regulation, Neoplastic</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">JNK Mitogen-Activated Protein Kinases</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Theoretical</style></keyword><keyword><style  face="normal" font="default" size="100%">Neuroblastoma</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018 Aug 22</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">13</style></volume><pages><style face="normal" font="default" size="100%">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;BACKGROUND: &lt;/b&gt;Despite the progress in neuroblastoma therapies the mortality of high-risk patients is still high (40-50%) and the molecular basis of the disease remains poorly known. Recently, a mathematical model was used to demonstrate that the network regulating stress signaling by the c-Jun N-terminal kinase pathway played a crucial role in survival of patients with neuroblastoma irrespective of their MYCN amplification status. This demonstrates the enormous potential of computational models of biological modules for the discovery of underlying molecular mechanisms of diseases.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Since signaling is known to be highly relevant in cancer, we have used a computational model of the whole cell signaling network to understand the molecular determinants of bad prognostic in neuroblastoma. Our model produced a comprehensive view of the molecular mechanisms of neuroblastoma tumorigenesis and progression.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;We have also shown how the activity of signaling circuits can be considered a reliable model-based prognostic biomarker.&lt;/p&gt;&lt;p&gt;&lt;b&gt;REVIEWERS: &lt;/b&gt;This article was reviewed by Tim Beissbarth, Wenzhong Xiao and Joanna Polanska. For the full reviews, please go to the Reviewers' comments section.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/30134948?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%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Erten, Cesim</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Graph-theoretical comparison of normal and tumor networks in identifying BRCA genes.</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%">Breast Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Regulatory Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Genes, Tumor Suppressor</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Theoretical</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 Nov 22</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">110</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;Identification of driver genes related to certain types of cancer is an important research topic. Several systems biology approaches have been suggested, in particular for the identification of breast cancer (BRCA) related genes. Such approaches usually rely on differential gene expression and/or mutational landscape data. In some cases interaction network data is also integrated to identify cancer-related modules computationally.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;We provide a framework for the comparative graph-theoretical analysis of networks integrating the relevant gene expression, mutations, and potein-protein interaction network data. The comparisons involve a graph-theoretical analysis of normal and tumor network pairs across all instances of a given set of breast cancer samples. The network measures under consideration are based on appropriate formulations of various centrality measures: betweenness, clustering coefficients, degree centrality, random walk distances, graph-theoretical distances, and Jaccard index centrality.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Among all the studied centrality-based graph-theoretical properties, we show that a betweenness-based measure differentiates BRCA genes across all normal versus tumor network pairs, than the rest of the popular centrality-based measures. The AUROC and AUPR values of the gene lists ordered with respect to the measures under study as compared to NCBI BioSystems pathway and the COSMIC database of cancer genes are the largest with the betweenness-based differentiation, followed by the measure based on degree centrality. In order to test the robustness of the suggested measures in prioritizing cancer genes, we further tested the two most promising measures, those based on betweenness and degree centralities, on randomly rewired networks. We show that both measures are quite resilient to noise in the input interaction network. We also compared the same measures against a state-of-the-art alternative disease gene prioritization method, MUFFFINN. We show that both our graph-theoretical measures outperform MUFFINN prioritizations in terms of ROC and precions/recall analysis. Finally, we filter the ordered list of the best measure, the betweenness-based differentiation, via a maximum-weight independent set formulation and investigate the top 50 genes in regards to literature verification. We show that almost all genes in the list are verified by the breast cancer literature and three genes are presented as novel genes that may potentialy be BRCA-related but missing in literature.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/29166896?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%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Formulating and testing hypotheses in functional genomics.</style></title><secondary-title><style face="normal" font="default" size="100%">Artif Intell Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Artif Intell Med</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Biological Evolution</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Genotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Theoretical</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2009 Feb-Mar</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">45</style></volume><pages><style face="normal" font="default" size="100%">97-107</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;OBJECTIVE: &lt;/b&gt;The ultimate goal of any genome-scale experiment is to provide a functional interpretation of the results, relating the available genomic information to the hypotheses that originated the experiment.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS AND RESULTS: &lt;/b&gt;Initially, this interpretation has been made on a pre-selection of relevant genes, based on the experimental values, followed by the study of the enrichment in some functional properties. Nevertheless, functional enrichment methods, demonstrated to have a flaw: the first step of gene selection was too stringent given that the cooperation among genes was ignored. The assumption that modules of genes related by relevant biological properties (functionality, co-regulation, chromosomal location, etc.) are the real actors of the cell biology lead to the development of new procedures, inspired in systems biology criteria, generically known as gene-set methods. These methods have been successfully used to analyze transcriptomic and large-scale genotyping experiments as well as to test other different genome-scale hypothesis in other fields such as phylogenomics.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2-3</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/18789659?dopt=Abstract</style></custom1></record></records></xml>