<?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%">Nueda, M. J.</style></author><author><style face="normal" font="default" size="100%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Westerhuis, J. A.</style></author><author><style face="normal" font="default" size="100%">Hoefsloot, H. C.</style></author><author><style face="normal" font="default" size="100%">Smilde, A. K.</style></author><author><style face="normal" font="default" size="100%">Talon, M.</style></author><author><style face="normal" font="default" size="100%">Ferrer, A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Discovering gene expression patterns in time course microarray experiments by ANOVA-SCA</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 *Analysis of Variance Computational Biology/*methods Computer Simulation Data Interpretation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistical Gene Expression Profiling/*methods Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistical Oligonucleotide Array Sequence Analysis/*methods Principal Component Analysis Time Factors Transcription</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</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=17519250</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">14</style></number><volume><style face="normal" font="default" size="100%">23</style></volume><pages><style face="normal" font="default" size="100%">1792-800</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">MOTIVATION: Designed microarray experiments are used to investigate the effects that controlled experimental factors have on gene expression and learn about the transcriptional responses associated with external variables. In these datasets, signals of interest coexist with varying sources of unwanted noise in a framework of (co)relation among the measured variables and with the different levels of the studied factors. Discovering experimentally relevant transcriptional changes require methodologies that take all these elements into account. RESULTS: In this work, we develop the application of the Analysis of variance-simultaneous component analysis (ANOVA-SCA) Smilde et al. Bioinformatics, (2005) to the analysis of multiple series time course microarray data as an example of multifactorial gene expression profiling experiments. We denoted this implementation as ASCA-genes. We show how the combination of ANOVA-modeling and a dimension reduction technique is effective in extracting targeted signals from data by-passing structural noise. The methodology is valuable for identifying main and secondary responses associated with the experimental factors and spotting relevant experimental conditions. We additionally propose a novel approach for gene selection in the context of the relation of individual transcriptional patterns to global gene expression signals. We demonstrate the methodology on both real and synthetic datasets. AVAILABILITY: ASCA-genes has been implemented in the statistical language R and is available at http://www.ivia.es/centrodegenomica/bioinformatics.htm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.</style></abstract><notes><style face="normal" font="default" size="100%">Nueda, Maria Jose Conesa, Ana Westerhuis, Johan A Hoefsloot, Huub C J Smilde, Age K Talon, Manuel Ferrer, Alberto Research Support, Non-U.S. Gov’t England Bioinformatics (Oxford, England) Bioinformatics. 2007 Jul 15;23(14):1792-800. Epub 2007 May 22.</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%">Hernandez, P.</style></author><author><style face="normal" font="default" size="100%">Huerta-Cepas, J.</style></author><author><style face="normal" font="default" size="100%">Montaner, D.</style></author><author><style face="normal" font="default" size="100%">Fatima Al-Shahrour</style></author><author><style face="normal" font="default" size="100%">Valls, J.</style></author><author><style face="normal" font="default" size="100%">Gomez, L.</style></author><author><style face="normal" font="default" size="100%">Capella, G.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">Pujana, M. A.</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></titles><keywords><keyword><style  face="normal" font="default" size="100%">*Cell Transformation</style></keyword><keyword><style  face="normal" font="default" size="100%">Biological Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Messenger/metabolism Signal Transduction Systems Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplastic *Gene Expression Profiling *Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplastic Humans Male Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistical Neoplasm Proteins/*physiology Neoplasms/etiology/*genetics Prostatic Neoplasms/genetics Protein Interaction Mapping RNA</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</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=17584915</style></url></web-urls></urls><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%">BACKGROUND: 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. RESULTS: 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. CONCLUSION: 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.</style></abstract><notes><style face="normal" font="default" size="100%">Hernandez, Pilar Huerta-Cepas, Jaime Montaner, David Al-Shahrour, Fatima Valls, Joan Gomez, Laia Capella, Gabriel Dopazo, Joaquin Pujana, Miguel Angel Research Support, Non-U.S. Gov’t England BMC genomics BMC Genomics. 2007 Jun 20;8:185.</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%">A. Conesa</style></author><author><style face="normal" font="default" size="100%">Nueda, M. J.</style></author><author><style face="normal" font="default" size="100%">Ferrer, A.</style></author><author><style face="normal" font="default" size="100%">Talon, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments</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 Computer Simulation Gene Expression/*physiology Gene Expression Profiling/*methods *Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistical Oligonucleotide Array Sequence Analysis/*methods *Software Time Factors</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</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=16481333</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">9</style></number><volume><style face="normal" font="default" size="100%">22</style></volume><pages><style face="normal" font="default" size="100%">1096-102</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">MOTIVATION: Multi-series time-course microarray experiments are useful approaches for exploring biological processes. In this type of experiments, the researcher is frequently interested in studying gene expression changes along time and in evaluating trend differences between the various experimental groups. The large amount of data, multiplicity of experimental conditions and the dynamic nature of the experiments poses great challenges to data analysis. RESULTS: In this work, we propose a statistical procedure to identify genes that show different gene expression profiles across analytical groups in time-course experiments. The method is a two-regression step approach where the experimental groups are identified by dummy variables. The procedure first adjusts a global regression model with all the defined variables to identify differentially expressed genes, and in second a variable selection strategy is applied to study differences between groups and to find statistically significant different profiles. The methodology is illustrated on both a real and a simulated microarray dataset.</style></abstract><notes><style face="normal" font="default" size="100%">Conesa, Ana Nueda, Maria Jose Ferrer, Alberto Talon, Manuel England Bioinformatics (Oxford, England) Bioinformatics. 2006 May 1;22(9):1096-102. Epub 2006 Feb 15.</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%">Arbiza, L.</style></author><author><style face="normal" font="default" size="100%">Duchi, S.</style></author><author><style face="normal" font="default" size="100%">Montaner, D.</style></author><author><style face="normal" font="default" size="100%">Burguet, J.</style></author><author><style face="normal" font="default" size="100%">Pantoja-Uceda, D.</style></author><author><style face="normal" font="default" size="100%">Pineda-Lucena, A.</style></author><author><style face="normal" font="default" size="100%">Dopazo, J.</style></author><author><style face="normal" font="default" size="100%">H. Dopazo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Selective pressures at a codon-level predict deleterious mutations in human disease genes</style></title><secondary-title><style face="normal" font="default" size="100%">J Mol Biol</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Amino Acid Sequence Amino Acid Substitution Codon/*genetics Databases</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Evolution</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Human Humans Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Inborn/*genetics Genome</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Genes</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Molecular Sequence Data *Mutation Neoplasms/genetics Proteins/genetics *Selection (Genetics) Tumor Suppressor Protein p53/chemistry/genetics</style></keyword><keyword><style  face="normal" font="default" size="100%">p53 Genetic Diseases</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</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=16584746</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">358</style></volume><pages><style face="normal" font="default" size="100%">1390-404</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Deleterious mutations affecting biological function of proteins are constantly being rejected by purifying selection from the gene pool. The non-synonymous/synonymous substitution rate ratio (omega) is a measure of selective pressure on amino acid replacement mutations for protein-coding genes. Different methods have been developed in order to predict non-synonymous changes affecting gene function. However, none has considered the estimation of selective constraints acting on protein residues. Here, we have used codon-based maximum likelihood models in order to estimate the selective pressures on the individual amino acid residues of a well-known model protein: p53. We demonstrate that the number of residues under strong purifying selection in p53 is much higher than those that are strictly conserved during the evolution of the species. In agreement with theoretical expectations, residues that have been noted to be of structural relevance, or in direct association with DNA, were among those showing the highest signals of purifying selection. Conversely, those changing according to a neutral, or nearly neutral mode of evolution, were observed to be irrelevant for protein function. Finally, using more than 40 human disease genes, we demonstrate that residues evolving under strong selective pressures (omega&lt;0.1) are significantly associated (p&lt;0.01) with human disease. We hypothesize that non-synonymous change on amino acids showing omega&lt;0.1 will most likely affect protein function. The application of this evolutionary prediction at a genomic scale will provide an a priori hypothesis of the phenotypic effect of non-synonymous coding single nucleotide polymorphisms (SNPs) in the human genome.</style></abstract><notes><style face="normal" font="default" size="100%">Arbiza, Leonardo Duchi, Serena Montaner, David Burguet, Jordi Pantoja-Uceda, David Pineda-Lucena, Antonio Dopazo, Joaquin Dopazo, Hernan Research Support, Non-U.S. Gov’t England Journal of molecular biology J Mol Biol. 2006 May 19;358(5):1390-404. Epub 2006 Mar 15.</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%">H. Dopazo</style></author><author><style face="normal" font="default" size="100%">Gordon, M. B.</style></author><author><style face="normal" font="default" size="100%">Perazzo, R.</style></author><author><style face="normal" font="default" size="100%">Risau-Gusman, S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A model for the emergence of adaptive subsystems</style></title><secondary-title><style face="normal" font="default" size="100%">Bull Math Biol</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">*Adaptation</style></keyword><keyword><style  face="normal" font="default" size="100%">Biological Algorithms Alleles Animals Evolution Genotype Humans *Learning *Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistical Neural Networks (Computer) Phenotype Synapses/genetics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2003</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=12597115</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">65</style></volume><pages><style face="normal" font="default" size="100%">27-56</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We investigate the interaction of learning and evolution in a changing environment. A stable learning capability is regarded as an emergent adaptive system evolved by natural selection of genetic variants. We consider the evolution of an asexual population. Each genotype can have ’fixed’ and ’flexible’ alleles. The former express themselves as synaptic connections that remain unchanged during ontogeny and the latter as synapses that can be adjusted through a learning algorithm. Evolution is modelled using genetic algorithms and the changing environment is represented by two optimal synaptic patterns that alternate a fixed number of times during the ’life’ of the individuals. The amplitude of the change is related to the Hamming distance between the two optimal patterns and the rate of change to the frequency with which both exchange roles. This model is an extension of that of Hinton and Nowlan in which the fitness is given by a probabilistic measure of the Hamming distance to the optimum. We find that two types of evolutionary pathways are possible depending upon how difficult (costly) it is to cope with the changes of the environment. In one case the population loses the learning ability, and the individuals inherit fixed synapses that are optimal in only one of the environmental states. In the other case a flexible subsystem emerges that allows the individuals to adapt to the changes of the environment. The model helps us to understand how an adaptive subsystem can emerge as the result of the tradeoff between the exploitation of a congenital structure and the exploration of the adaptive capabilities practised by learning.</style></abstract><notes><style face="normal" font="default" size="100%">Dopazo, H Gordon, M B Perazzo, R Risau-Gusman, S Research Support, Non-U.S. Gov’t United States Bulletin of mathematical biology Bull Math Biol. 2003 Jan;65(1):27-56.</style></notes></record></records></xml>