@article {1203,
	title = {Actionable pathways: interactive discovery of therapeutic targets using signaling pathway models.},
	journal = {Nucleic acids research},
	year = {2016},
	month = {2016 May 2},
	abstract = {The discovery of actionable targets is crucial for targeted therapies and is also a constituent part of the drug discovery process. The success of an intervention over a target depends critically on its contribution, within the complex network of gene interactions, to the cellular processes responsible for disease progression or therapeutic response. Here we present PathAct, a web server that predicts the effect that interventions over genes (inhibitions or activations that simulate knock-outs, drug treatments or over-expressions) can have over signal transmission within signaling pathways and, ultimately, over the cell functionalities triggered by them. PathAct implements an advanced graphical interface that provides a unique interactive working environment in which the suitability of potentially actionable genes, that could eventually become drug targets for personalized or individualized therapies, can be easily tested. The PathAct tool can be found at: http://pathact.babelomics.org.},
	keywords = {actionable genes, Disease mechanism, drug action mechanism, Drug discovery, pathway analysis, personalized medicine, signalling, therapeutic targets},
	issn = {1362-4962},
	doi = {10.1093/nar/gkw369},
	url = {http://nar.oxfordjournals.org/content/early/2016/05/02/nar.gkw369.full},
	author = {Salavert, Francisco and Hidago, Marta R and Amadoz, Alicia and Cubuk, Cankut and Medina, Ignacio and Crespo, Daniel and Carbonell-Caballero, Jos{\'e} and Joaqu{\'\i}n Dopazo}
}
@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}
}
