@article {403,
	title = {A comparison of mechanistic signaling pathway activity analysis methods.},
	journal = {Brief Bioinform},
	volume = {20},
	year = {2019},
	month = {2019 Sep 27},
	pages = {1655-1668},
	abstract = {<p>Understanding the aspects of cell functionality that account for disease mechanisms or drug modes of action is a main challenge for precision medicine. Classical gene-based approaches ignore the modular nature of most human traits, whereas conventional pathway enrichment approaches produce only illustrative results of limited practical utility. Recently, a family of new methods has emerged that change the focus from the whole pathways to the definition of elementary subpathways within them that have any mechanistic significance and to the study of their activities. Thus, mechanistic pathway activity (MPA) methods constitute a new paradigm that allows recoding poorly informative genomic measurements into cell activity quantitative values and relate them to phenotypes. Here we provide a review on the MPA methods available and explain their contribution to systems medicine approaches for addressing challenges in the diagnostic and treatment of complex diseases.</p>},
	keywords = {Algorithms, Humans, Postmortem Changes, Signal Transduction, Systems biology, Transcriptome},
	issn = {1477-4054},
	doi = {10.1093/bib/bby040},
	author = {Amadoz, Alicia and Hidalgo, Marta R and Cubuk, Cankut and Carbonell-Caballero, Jos{\'e} and Dopazo, Joaquin}
}
@article {474,
	title = {Using activation status of signaling pathways as mechanism-based biomarkers to predict drug sensitivity.},
	journal = {Sci Rep},
	volume = {5},
	year = {2015},
	month = {2015 Dec 18},
	pages = {18494},
	abstract = {<p>Many complex traits, as drug response, are associated with changes in biological pathways rather than being caused by single gene alterations. Here, a predictive framework is presented in which gene expression data are recoded into activity statuses of signal transduction circuits (sub-pathways within signaling pathways that connect receptor proteins to final effector proteins that trigger cell actions). Such activity values are used as features by a prediction algorithm which can efficiently predict a continuous variable such as the IC50 value. The main advantage of this prediction method is that the features selected by the predictor, the signaling circuits, are themselves rich-informative, mechanism-based biomarkers which provide insight into or drug molecular mechanisms of action (MoA). </p>},
	keywords = {Algorithms, Antineoplastic Agents, biomarkers, Cell Line, Tumor, Cell Survival, gene expression, Humans, Lethal Dose 50, Neoplasms, Phosphorylation, Proteins, Signal Transduction},
	issn = {2045-2322},
	doi = {10.1038/srep18494},
	author = {Amadoz, Alicia and Sebasti{\'a}n-Leon, Patricia and Vidal, Enrique and Salavert, Francisco and Dopazo, Joaquin}
}
