02621nas a2200325   4500008004100000022001400041245012100055210006900176260001600245300000900261490000700270520154400277653002101821653001901842653002901861653002001890653003401910653001301944653001101957653003201968100002402000700002002024700001902044700001902063700002402082700001902106700002002125700002002145856013002165       2012                        eng d  a1362-496200aDiscovering the hidden sub-network component in a ranked list of genes or proteins derived from genomic experiments.0 aDiscovering the hidden subnetwork component in a ranked list of   c2012 Nov 01  ae1580 v403 a<p>Genomic experiments (e.g. differential gene expression, single-nucleotide polymorphism association) typically produce ranked list of genes. We present a simple but powerful approach which uses protein-protein interaction data to detect sub-networks within such ranked lists of genes or proteins. We performed an exhaustive study of network parameters that allowed us concluding that the average number of components and the average number of nodes per component are the parameters that best discriminate between real and random networks. A novel aspect that increases the efficiency of this strategy in finding sub-networks is that, in addition to direct connections, also connections mediated by intermediate nodes are considered to build up the sub-networks. The possibility of using of such intermediate nodes makes this approach more robust to noise. It also overcomes some limitations intrinsic to experimental designs based on differential expression, in which some nodes are invariant across conditions. The proposed approach can also be used for candidate disease-gene prioritization. Here, we demonstrate the usefulness of the approach by means of several case examples that include a differential expression analysis in Fanconi Anemia, a genome-wide association study of bipolar disorder and a genome-scale study of essentiality in cancer genes. An efficient and easy-to-use web interface (available at http://www.babelomics.org) based on HTML5 technologies is also provided to run the algorithm and represent the network.</p>10aBipolar Disorder10aFanconi Anemia10aGene Regulatory Networks10aGenes, Neoplasm10aGenome-Wide Association Study10aGenomics10aHumans10aProtein Interaction Mapping1 aGarcía-Alonso, Luz1 aAlonso, Roberto1 aVidal, Enrique1 aAmadoz, Alicia1 aDe Maria, Alejandro1 aMinguez, Pablo1 aMedina, Ignacio1 aDopazo, Joaquin  uhttps://www.clinbioinfosspa.es/content/discovering-hidden-sub-network-component-ranked-list-genes-or-proteins-derived-genomic01719nas a2200229   4500008004100000022001400041245009600055210006900151260001300220300001000233490000600243520095200249653003101201653002901232653001301261653002001274653001301294653002001307100001901327700002001346856012301366       2010                        eng d  a1744-838700aFunctional genomics and networks: new approaches in the extraction of complex gene modules.0 aFunctional genomics and networks new approaches in the extractio  c2010 Feb  a55-630 v73 a<p>The engine that makes the cell work is made of an intricate network of molecular interactions. Nowadays, the elements and relationships of this complex network can be studied with several types of high-throughput techniques. The dream of having a global picture of the cell from different perspectives that can jointly explain cell behavior is, at least technically, feasible. However, this task can only be accomplished by filling the gap between data and information. The availability of methods capable of accurately managing, integrating and analyzing the results from these experiments is crucial for this purpose. Here, we review the new challenges raised by the availability of different genomic data, as well as the new proposals presented to cope with the increasing data complexity. Special emphasis is given to approaches that explore the transcriptome trying to describe the modules of genes that account for the traits studied.</p>10aGene Expression Regulation10aGene Regulatory Networks10aGenomics10aProtein Binding10aProteins10aSystems biology1 aMinguez, Pablo1 aDopazo, Joaquin  uhttps://www.clinbioinfosspa.es/content/functional-genomics-and-networks-new-approaches-extraction-complex-gene-modules