@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 {552,
	title = {Functional genomics and networks: new approaches in the extraction of complex gene modules.},
	journal = {Expert Rev Proteomics},
	volume = {7},
	year = {2010},
	month = {2010 Feb},
	pages = {55-63},
	abstract = {<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>},
	keywords = {Gene Expression Regulation, Gene Regulatory Networks, Genomics, Protein Binding, Proteins, Systems biology},
	issn = {1744-8387},
	doi = {10.1586/epr.09.103},
	author = {Minguez, Pablo and Dopazo, Joaquin}
}
