@article {512,
	title = {Discovering the hidden sub-network component in a ranked list of genes or proteins derived from genomic experiments.},
	journal = {Nucleic Acids Res},
	volume = {40},
	year = {2012},
	month = {2012 Nov 01},
	pages = {e158},
	abstract = {<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>},
	keywords = {Bipolar Disorder, Fanconi Anemia, Gene Regulatory Networks, Genes, Neoplasm, Genome-Wide Association Study, Genomics, Humans, Protein Interaction Mapping},
	issn = {1362-4962},
	doi = {10.1093/nar/gks699},
	author = {Garc{\'\i}a-Alonso, Luz and Alonso, Roberto and Vidal, Enrique and Amadoz, Alicia and De Maria, Alejandro and Minguez, Pablo and Medina, Ignacio and Dopazo, Joaquin}
}
@article {517,
	title = {Inferring the regulatory network behind a gene expression experiment.},
	journal = {Nucleic Acids Res},
	volume = {40},
	year = {2012},
	month = {2012 Jul},
	pages = {W168-72},
	abstract = {<p>Transcription factors (TFs) and miRNAs are the most important dynamic regulators in the control of gene expression in multicellular organisms. These regulatory elements play crucial roles in development, cell cycling and cell signaling, and they have also been associated with many diseases. The Regulatory Network Analysis Tool (RENATO) web server makes the exploration of regulatory networks easy, enabling a better understanding of functional modularity and network integrity under specific perturbations. RENATO is suitable for the analysis of the result of expression profiling experiments. The program analyses lists of genes and search for the regulators compatible with its activation or deactivation. Tests of single enrichment or gene set enrichment allow the selection of the subset of TFs or miRNAs significantly involved in the regulation of the query genes. RENATO also offers an interactive advanced graphical interface that allows exploring the regulatory network found.RENATO is available at: http://renato.bioinfo.cipf.es/.</p>},
	keywords = {Binding Sites, Databases, Genetic, Fanconi Anemia, Gene Regulatory Networks, Internet, MicroRNAs, Software, Transcription Factors, Transcriptome},
	issn = {1362-4962},
	doi = {10.1093/nar/gks573},
	author = {Bleda, Marta and Medina, Ignacio and Alonso, Roberto and De Maria, Alejandro and Salavert, Francisco and Dopazo, Joaquin}
}
