@article {397,
	title = {The effects of death and post-mortem cold ischemia on human tissue transcriptomes.},
	journal = {Nat Commun},
	volume = {9},
	year = {2018},
	month = {2018 Feb 13},
	pages = {490},
	abstract = {<p>Post-mortem tissues samples are a key resource for investigating patterns of gene expression. However, the processes triggered by death and the post-mortem interval (PMI) can significantly alter physiologically normal RNA levels. We investigate the impact of PMI on gene expression using data from multiple tissues of post-mortem donors obtained from the GTEx project. We find that many genes change expression over relatively short PMIs in a tissue-specific manner, but this potentially confounding effect in a biological analysis can be minimized by taking into account appropriate covariates. By comparing ante- and post-mortem blood samples, we identify the cascade of transcriptional events triggered by death of the organism. These events do not appear to simply reflect stochastic variation resulting from mRNA degradation, but active and ongoing regulation of transcription. Finally, we develop a model to predict the time since death from the analysis of the transcriptome of a few readily accessible tissues.</p>},
	keywords = {Blood, Cold Ischemia, Death, Female, gene expression, Humans, Models, Biological, Postmortem Changes, RNA, Messenger, Stochastic Processes, Transcriptome},
	issn = {2041-1723},
	doi = {10.1038/s41467-017-02772-x},
	author = {Ferreira, Pedro G and Mu{\~n}oz-Aguirre, Manuel and Reverter, Ferran and S{\'a} Godinho, Caio P and Sousa, Abel and Amadoz, Alicia and Sodaei, Reza and Hidalgo, Marta R and Pervouchine, Dmitri and Carbonell-Caballero, Jos{\'e} and Nurtdinov, Ramil and Breschi, Alessandra and Amador, Raziel and Oliveira, Patr{\'\i}cia and Cubuk, Cankut and Curado, Jo{\~a}o and Aguet, Fran{\c c}ois and Oliveira, Carla and Dopazo, Joaquin and Sammeth, Michael and Ardlie, Kristin G and Guig{\'o}, Roderic}
}
@article {434,
	title = {High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes.},
	journal = {Oncotarget},
	volume = {8},
	year = {2017},
	month = {2017 Jan 17},
	pages = {5160-5178},
	abstract = {<p>Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is a main challenge for precision medicine. Here we propose a new method that models cell signaling using biological knowledge on signal transduction. The method recodes individual gene expression values (and/or gene mutations) into accurate measurements of changes in the activity of signaling circuits, which ultimately constitute high-throughput estimations of cell functionalities caused by gene activity within the pathway. Moreover, such estimations can be obtained either at cohort-level, in case/control comparisons, or personalized for individual patients. The accuracy of the method is demonstrated in an extensive analysis involving 5640 patients from 12 different cancer types. Circuit activity measurements not only have a high diagnostic value but also can be related to relevant disease outcomes such as survival, and can be used to assess therapeutic interventions.</p>},
	keywords = {Computational Biology, gene expression, Gene Regulatory Networks, Humans, mutation, Neoplasms, Precision Medicine, Sequence Analysis, RNA, Signal Transduction},
	issn = {1949-2553},
	doi = {10.18632/oncotarget.14107},
	author = {Hidalgo, Marta R and Cubuk, Cankut and Amadoz, Alicia and Salavert, Francisco and Carbonell-Caballero, Jos{\'e} and Dopazo, Joaquin}
}
