02102nas a2200217   4500008004100000245007500041210006900116300001200185490000700197520140300204653001001607653002901617100001601646700002301662700002401685700001401709700001501723700002501738700001501763856010601778       2004                        eng d00aNew challenges in gene expression data analysis and the extended GEPAS0 aNew challenges in gene expression data analysis and the extended  aW485-910 v323 a<p>Since the first papers published in the late nineties, including, for the first time, a comprehensive analysis of microarray data, the number of questions that have been addressed through this technique have both increased and diversified. Initially, interest focussed on genes coexpressing across sets of experimental conditions, implying, essentially, the use of clustering techniques. Recently, however, interest has focussed more on finding genes differentially expressed among distinct classes of experiments, or correlated to diverse clinical outcomes, as well as in building predictors. In addition to this, the availability of accurate genomic data and the recent implementation of CGH arrays has made mapping expression and genomic data on the chromosomes possible. There is also a clear demand for methods that allow the automatic transfer of biological information to the results of microarray experiments. Different initiatives, such as the Gene Ontology (GO) consortium, pathways databases, protein functional motifs, etc., provide curated annotations for genes. Whereas many resources on the web focus mainly on clustering methods, GEPAS has evolved to cope with the aforementioned new challenges that have recently arisen in the field of microarray data analysis. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://gepas.bioinfo.cnio.es.</p>10agepas10amicroarray data analysis1 aHerrero, J.1 aVaquerizas, J., M.1 aAl-Shahrour, Fatima1 aConde, L.1 aMateos, A.1 aDiaz-Uriarte, J., S.1 aDopazo, J.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1521543402356nas a2200265   4500008004100000245006200041210006200103300001000165490000700175520124200182653003001424653007301454653007501527653004901602653004201651653004301693653005001736653007501786653005801861100001901919700001601938700001501954700001501969856010601984       2003                        eng d00aComparing bacterial genomes through conservation profiles0 aComparing bacterial genomes through conservation profiles  a991-80 v133 aWe constructed two-dimensional representations of profiles of gene conservation across different genomes using the genome of Escherichia coli as a model. These profiles permit both the visualization at the genome level of different traits in the organism studied and, at the same time, reveal features related to the genomes analyzed (such as defective genomes or genomes that lack a particular system). Conserved genes are not uniformly distributed along the E. coli genome but tend to cluster together. The study of gene distribution patterns across genomes is important for the understanding of how sets of genes seem to be dependent on each other, probably having some functional link. This provides additional evidence that can be used for the elucidation of the function of unannotated genes. Clustering these patterns produces families of genes which can be arranged in a hierarchy of closeness. In this way, functions can be defined at different levels of generality depending on the level of the hierarchy that is studied. The combined study of conservation and phenotypic traits opens up the possibility of defining phenotype/genotype associations, and ultimately inferring the gene or genes responsible for a particular trait.10aBacterial Genotype Models10aBacterial/genetics Cluster Analysis Conserved Sequence/*genetics DNA10aBacterial/genetics Escherichia coli/classification/*genetics Evolution10aBacterial/genetics Gene Order/genetics Genes10aBacterial/genetics/physiology *Genome10aChromosome Mapping/methods Chromosomes10aGenetic Phenotype Phylogeny Sequence Homology10aMolecular Gene Expression Profiling/methods Gene Expression Regulation10aNucleic Acid Species Specificity Terminology as Topic1 aMartin, M., J.1 aHerrero, J.1 aMateos, A.1 aDopazo, J.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1269532401500nas a2200217   4500008004100000245007700041210006900118300001100187490000700198520080200205653001001007653002901017100001601046700002401062700002101086700001501107700002301122700001601145700001501161856010601176       2003                        eng d00aGEPAS: A web-based resource for microarray gene expression data analysis0 aGEPAS A webbased resource for microarray gene expression data an  a3461-70 v313 a<p>We present a web-based pipeline for microarray gene expression profile analysis, GEPAS, which stands for Gene Expression Profile Analysis Suite (http://gepas.bioinfo.cnio.es). GEPAS is composed of different interconnected modules which include tools for data pre-processing, two-conditions comparison, unsupervised and supervised clustering (which include some of the most popular methods as well as home made algorithms) and several tests for differential gene expression among different classes, continuous variables or survival analysis. A multiple purpose tool for data mining, based on Gene Ontology, is also linked to the tools, which constitutes a very convenient way of analysing clustering results. On-line tutorials are available from our main web server (http://bioinfo.cnio.es).</p>10agepas10amicroarray data analysis1 aHerrero, J.1 aAl-Shahrour, Fatima1 aDiaz-Uriarte, R.1 aMateos, A.1 aVaquerizas, J., M.1 aSantoyo, J.1 aDopazo, J.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1282434500720nas a2200181   4500008004100000245013000041210006900171260003000240300001000270653001500280100002400295700001600319700001500335700001600350700002100366700001500387856013600402       2003                        eng d00aUsing Gene Ontology on genome-scale studies to find significant associations of biologically relevant terms to group of genes0 aUsing Gene Ontology on genomescale studies to find significant a  aNew York, USAbIEEE Press  a43-5210ababelomics1 aAl-Shahrour, Fatima1 aHerrero, J.1 aMateos, A.1 aSantoyo, J.1 aDíaz-Uriarte, R1 aDopazo, J.  uhttps://www.clinbioinfosspa.es/content/using-gene-ontology-genome-scale-studies-find-significant-associations-biologically-relevant00612nas a2200145   4500008004100000245013500041210006900176260002000245300001100265100001500276700001600291700001600307700001500323856012800338       2002                        eng d00aSupervised Neural Networks For Clustering Conditions In DNA Array Data After Reducing Noise By Clustering Gene Expression Profiles0 aSupervised Neural Networks For Clustering Conditions In DNA Arra  bKluwer Academic  a91-1031 aMateos, A.1 aHerrero, J.1 aTamames, J.1 aDopazo, J.  uhttps://www.clinbioinfosspa.es/content/supervised-neural-networks-clustering-conditions-dna-array-data-after-reducing-noise02866nas a2200193   4500008004100000245011400041210006900155300001200224490000700236520197100243653025902214100001502473700001502488700001502503700001102518700001702529700002002546856010602566       2002                        eng d00aSystematic learning of gene functional classes from DNA array expression data by using multilayer perceptrons0 aSystematic learning of gene functional classes from DNA array ex  a1703-150 v123 aRecent advances in microarray technology have opened new ways for functional annotation of previously uncharacterised genes on a genomic scale. This has been demonstrated by unsupervised clustering of co-expressed genes and, more importantly, by supervised learning algorithms. Using prior knowledge, these algorithms can assign functional annotations based on more complex expression signatures found in existing functional classes. Previously, support vector machines (SVMs) and other machine-learning methods have been applied to a limited number of functional classes for this purpose. Here we present, for the first time, the comprehensive application of supervised neural networks (SNNs) for functional annotation. Our study is novel in that we report systematic results for  100 classes in the Munich Information Center for Protein Sequences (MIPS) functional catalog. We found that only  10% of these are learnable (based on the rate of false negatives). A closer analysis reveals that false positives (and negatives) in a machine-learning context are not necessarily "false" in a biological sense. We show that the high degree of interconnections among functional classes confounds the signatures that ought to be learned for a unique class. We term this the "Borges effect" and introduce two new numerical indices for its quantification. Our analysis indicates that classification systems with a lower Borges effect are better suitable for machine learning. Furthermore, we introduce a learning procedure for combining false positives with the original class. We show that in a few iterations this process converges to a gene set that is learnable with considerably low rates of false positives and negatives and contains genes that are biologically related to the original class, allowing for a coarse reconstruction of the interactions between associated biological pathways. We exemplify this methodology using the well-studied tricarboxylic acid cycle.10aAlgorithms Artificial Intelligence Citric Acid Cycle/genetics Cluster Analysis Computational Biology/methods Gene Expression Profiling/*methods/statistics & numerical data Genes/*physiology Genetic Heterogeneity Neural Networks (Computer) Oligonucleotide1 aMateos, A.1 aDopazo, J.1 aJansen, R.1 aTu, Y.1 aGerstein, M.1 aStolovitzky, G.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1242175700621nas a2200133   4500008004100000245016500041210006900206260002000275300001200295100001500307700001600322700001500338856013400353       2002                        eng d00aUsing perceptrons for supervised classification of DNA microarray samples: obtaining the optimal level of information and finding differentially expressed genes0 aUsing perceptrons for supervised classification of DNA microarra  bJ.R. Dorronsoro  a577-5821 aMateos, A.1 aHerrero, J.1 aDopazo, J.  uhttps://www.clinbioinfosspa.es/content/using-perceptrons-supervised-classification-dna-microarray-samples-obtaining-optimal-level