@article {1128,
	title = {Assessing the impact of mutations found in next generation sequencing data over human signaling pathways.},
	journal = {Nucleic acids research},
	volume = {43},
	number = {W1},
	year = {2015},
	month = {2015 Apr 16},
	pages = {W270-W275},
	abstract = {Modern sequencing technologies produce increasingly detailed data on genomic variation. However, conventional methods for relating either individual variants or mutated genes to phenotypes present known limitations given the complex, multigenic nature of many diseases or traits. Here we present PATHiVar, a web-based tool that integrates genomic variation data with gene expression tissue information. PATHiVar constitutes a new generation of genomic data analysis methods that allow studying variants found in next generation sequencing experiment in the context of signaling pathways. Simple Boolean models of pathways provide detailed descriptions of the impact of mutations in cell functionality so as, recurrences in functionality failures can easily be related to diseases, even if they are produced by mutations in different genes. Patterns of changes in signal transmission circuits, often unpredictable from individual genes mutated, correspond to patterns of affected functionalities that can be related to complex traits such as disease progression, drug response, etc. PATHiVar is available at: http://pathivar.babelomics.org.},
	keywords = {NGS, pathways, signalling, Systems biology},
	issn = {1362-4962},
	doi = {10.1093/nar/gkv349},
	url = {http://nar.oxfordjournals.org/content/43/W1/W270},
	author = {Hernansaiz-Ballesteros, Rosa D and Salavert, Francisco and Sebasti{\'a}n-Leon, Patricia and Alem{\'a}n, Alejandro and Medina, Ignacio and Joaqu{\'\i}n Dopazo}
}
@article {1129,
	title = {Babelomics 5.0: functional interpretation for new generations of genomic data.},
	journal = {Nucleic acids research},
	volume = {43},
	number = {W1},
	year = {2015},
	month = {2015 Apr 20},
	pages = {W117-W121},
	abstract = {Babelomics has been running for more than one decade offering a user-friendly interface for the functional analysis of gene expression and genomic data. Here we present its fifth release, which includes support for Next Generation Sequencing data including gene expression (RNA-seq), exome or genome resequencing. Babelomics has simplified its interface, being now more intuitive. Improved visualization options, such as a genome viewer as well as an interactive network viewer, have been implemented. New technical enhancements at both, client and server sides, makes the user experience faster and more dynamic. Babelomics offers user-friendly access to a full range of methods that cover: (i) primary data analysis, (ii) a variety of tests for different experimental designs and (iii) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context. In addition to the public server, local copies of Babelomics can be downloaded and installed. Babelomics is freely available at: http://www.babelomics.org.},
	keywords = {babelomics, data integration, gene set analysis, interactome, network analysis, NGS, RNA-seq, Systems biology, transcriptomics},
	issn = {1362-4962},
	doi = {10.1093/nar/gkv384},
	url = {http://nar.oxfordjournals.org/content/43/W1/W117},
	author = {Alonso, Roberto and Salavert, Francisco and Garcia-Garcia, Francisco and Carbonell-Caballero, Jos{\'e} and Bleda, Marta and Garc{\'\i}a-Alonso, Luz and Sanchis-Juan, Alba and Perez-Gil, Daniel and Marin-Garcia, Pablo and S{\'a}nchez, Rub{\'e}n and Cubuk, Cankut and Hidalgo, Marta R and Amadoz, Alicia and Hernansaiz-Ballesteros, Rosa D and Alem{\'a}n, Alejandro and T{\'a}rraga, Joaqu{\'\i}n and Montaner, David and Medina, Ignacio and Dopazo, Joaquin}
}
@article {1093,
	title = {Understanding disease mechanisms with models of signaling pathway activities.},
	journal = {BMC systems biology},
	volume = {8},
	year = {2014},
	month = {2014 Oct 25},
	pages = {121},
	abstract = {BackgroundUnderstanding the aspects of the cell functionality that account for disease or drug action mechanisms is one of the main challenges in the analysis of genomic data and is on the basis of the future implementation of precision medicine.ResultsHere we propose a simple probabilistic model in which signaling pathways are separated into elementary sub-pathways or signal transmission circuits (which ultimately trigger cell functions) and then transforms gene expression measurements into probabilities of activation of such signal transmission circuits. Using this model, differential activation of such circuits between biological conditions can be estimated. Thus, circuit activation statuses can be interpreted as biomarkers that discriminate among the compared conditions. This type of mechanism-based biomarkers accounts for cell functional activities and can easily be associated to disease or drug action mechanisms. The accuracy of the proposed model is demonstrated with simulations and real datasets.ConclusionsThe proposed model provides detailed information that enables the interpretation disease mechanisms as a consequence of the complex combinations of altered gene expression values. Moreover, it offers a framework for suggesting possible ways of therapeutic intervention in a pathologically perturbed system.},
	keywords = {Disease mechanism, pathway, signalling, Systems biology},
	issn = {1752-0509},
	doi = {10.1186/s12918-014-0121-3},
	url = {http://www.biomedcentral.com/1752-0509/8/121/abstract},
	author = {Sebasti{\'a}n-Leon, Patricia and Vidal, Enrique and Minguez, Pablo and Ana Conesa and Sonia Tarazona and Amadoz, Alicia and Armero, Carmen and Salavert, Francisco and Vidal-Puig, Antonio and Montaner, David and Joaqu{\'\i}n Dopazo}
}
