02714nas a2200253   4500008004100000245009200041210006900133300001300202490000700215520168800222653010801910653001202018653001902030653005802049653012102107100001802228700001502246700002302261700002202284700001902306700001402325700001502339856010602354       2007                        eng d00aDiscovering gene expression patterns in time course microarray experiments by ANOVA-SCA0 aDiscovering gene expression patterns in time course microarray e  a1792-8000 v233 aMOTIVATION: 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.10aAlgorithms *Analysis of Variance Computational Biology/*methods Computer Simulation Data Interpretation10aGenetic10aGenetic Models10aStatistical Gene Expression Profiling/*methods Models10aStatistical Oligonucleotide Array Sequence Analysis/*methods Principal Component Analysis Time Factors Transcription1 aNueda, M., J.1 aConesa, A.1 aWesterhuis, J., A.1 aHoefsloot, H., C.1 aSmilde, A., K.1 aTalon, M.1 aFerrer, A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1751925002784nas a2200301   4500008004100000245006900041210006800110300000800178490000600186520165900192653002501851653002201876653001901898653006101917653007001978653003402048653013602082100001802218700002102236700001702257700002402274700001402298700001402312700001602326700001502342700001902357856010602376       2007                        eng d00aEvidence for systems-level molecular mechanisms of tumorigenesis0 aEvidence for systemslevel molecular mechanisms of tumorigenesis  a1850 v83 aBACKGROUND: 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 "CGPs") 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.10a*Cell Transformation10aBiological Models10aGenetic Models10aMessenger/metabolism Signal Transduction Systems Biology10aNeoplastic *Gene Expression Profiling *Gene Expression Regulation10aNeoplastic Humans Male Models10aStatistical Neoplasm Proteins/*physiology Neoplasms/etiology/*genetics Prostatic Neoplasms/genetics Protein Interaction Mapping RNA1 aHernandez, P.1 aHuerta-Cepas, J.1 aMontaner, D.1 aAl-Shahrour, Fatima1 aValls, J.1 aGomez, L.1 aCapella, G.1 aDopazo, J.1 aPujana, M., A.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1758491501934nas a2200193   4500008004100000245012000041210006900161300001300230490000700243520110800250653010701358653001901465653008801484100001501572700001801587700001501605700001401620856010601634       2006                        eng d00amaSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments0 amaSigPro a method to identify significantly differential express  a1096-1020 v223 aMOTIVATION: 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.10a*Algorithms Computer Simulation Gene Expression/*physiology Gene Expression Profiling/*methods *Models10aGenetic Models10aStatistical Oligonucleotide Array Sequence Analysis/*methods *Software Time Factors1 aConesa, A.1 aNueda, M., J.1 aFerrer, A.1 aTalon, M.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1648133302859nas a2200301   4500008004100000245009400041210006900135300001300204490000800217520172300225653007401948653002202022653001902044653002402063653002802087653002002115653015502135653002502290100001502315700001402330700001702344700001602361700002202377700002202399700001502421700001502436856010602451       2006                        eng d00aSelective pressures at a codon-level predict deleterious mutations in human disease genes0 aSelective pressures at a codonlevel predict deleterious mutation  a1390-4040 v3583 aDeleterious 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<0.1) are significantly associated (p<0.01) with human disease. We hypothesize that non-synonymous change on amino acids showing omega<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.10aAmino Acid Sequence Amino Acid Substitution Codon/*genetics Databases10aGenetic Evolution10aGenetic Models10aHuman Humans Models10aInborn/*genetics Genome10aMolecular Genes10aMolecular Molecular Sequence Data *Mutation Neoplasms/genetics Proteins/genetics *Selection (Genetics) Tumor Suppressor Protein p53/chemistry/genetics10ap53 Genetic Diseases1 aArbiza, L.1 aDuchi, S.1 aMontaner, D.1 aBurguet, J.1 aPantoja-Uceda, D.1 aPineda-Lucena, A.1 aDopazo, J.1 aDopazo, H.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1658474602406nas a2200205   4500008004100000245005300041210005100094300001000145490000700155520166900162653001601831653008601847653001901933653007101952100001502023700001902038700001602057700002102073856010602094       2003                        eng d00aA model for the emergence of adaptive subsystems0 amodel for the emergence of adaptive subsystems  a27-560 v653 aWe 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.10a*Adaptation10aBiological Algorithms Alleles Animals Evolution Genotype Humans *Learning *Models10aGenetic Models10aStatistical Neural Networks (Computer) Phenotype Synapses/genetics1 aDopazo, H.1 aGordon, M., B.1 aPerazzo, R.1 aRisau-Gusman, S.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12597115