@article {664,
	title = {Using mechanistic models for the clinical interpretation of complex genomic variation},
	journal = {Scientific Reports},
	volume = {9},
	year = {2019},
	month = {Jan-12-2019},
	doi = {10.1038/s41598-019-55454-7},
	url = {http://www.nature.com/articles/s41598-019-55454-7http://www.nature.com/articles/s41598-019-55454-7.pdfhttp://www.nature.com/articles/s41598-019-55454-7.pdfhttp://www.nature.com/articles/s41598-019-55454-7},
	author = {Pe{\~n}a-Chilet, Maria and Esteban-Medina, Marina and Falco, Matias M. and Rian, Kinza and Hidalgo, Marta R. and Loucera, Carlos 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}
}
@article {565,
	title = {Understanding disease mechanisms with models of signaling pathway activities},
	journal = {BMC systems biology},
	volume = {8},
	year = {2014},
	month = {10},
	pages = {121},
	doi = {10.1186/s12918-014-0121-3},
	author = {Sebasti{\'a}n-Leon, Patricia and Vidal, Enrique and Minguez, Pablo and Conesa, Ana and Tarazona, Sonia and Amadoz, Alicia and Armero, Carmen and Salavert Torres, Francisco and Vidal-Puig, Antonio and Montaner, David and Dopazo, Joaquin}
}
@article {900,
	title = {Using GPUs for the Exact Alignment of Short-read Genetic Sequences by Means of the Burrows{\textendash}Wheeler Transform.},
	journal = {IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM},
	volume = {9},
	number = {4},
	year = {2012},
	month = {2012 Mar 20},
	pages = {1245-1256},
	abstract = {General Purpose Graphic Processing Units (GPGPUs) constitute an inexpensive resource for computing-intensive applications that could exploit an intrinsic fine-grain parallelism. This paper presents the design and implementation in GPGPUs of an exact alignment tool for nucleotide sequences based on the Burrows-Wheeler Transform. We compare this algorithm with state-of-the-art implementations of the same algorithm over standard CPUs, and considering the same conditions in terms of I/O. Excluding disk transfers, the implementation of the algorithm in GPUs shows a speedup larger than 12x, when compared to CPU execution. This implementation exploits the parallelism by concurrently searching different sequences on the same reference search tree, maximising memory locality and ensuring a symmetric access to the data. The article describes the behaviour of the algorithm in GPU, showing a good scalability in the performance, only limited by the size of the GPU inner memory.},
	keywords = {Burrows-Wheeler transform, CPU execution, GPGPU, NGS},
	issn = {1557-9964},
	doi = {10.1109/TCBB.2012.49},
	url = {http://ieeexplore.ieee.org.sire.ub.edu/xpl/articleDetails.jsp?reload=true\&arnumber=6175888},
	author = {Salavert Torres, Jose and Blanquer Espert, Ignacio and Tomas Dominguez, Andres and Hernendez, Vicente and Medina, Ignacio and Terraga, Joaquin and Dopazo, Joaquin}
}
@article {584,
	title = {On the Use of Functional Module Definitions in the Analysis of Genomic Experiments},
	journal = {Molecular and Cellular Toxicology},
	volume = {5},
	year = {2009},
	month = {09},
	pages = {47-47},
	author = {Dopazo, Joaquin}
}
@article {600,
	title = {Use of estimated evolutionary strength at the codon level improves the prediction of disease-related protein mutations in humans.},
	journal = {Hum Mutat},
	volume = {29},
	year = {2008},
	month = {2008 Jan},
	pages = {198-204},
	abstract = {<p>Predicting the functional impact of protein variation is one of the most challenging problems in bioinformatics. A rapidly growing number of genome-scale studies provide large amounts of experimental data, allowing the application of rigorous statistical approaches for predicting whether a given single point mutation has an impact on human health. Up until now, existing methods have limited their source data to either protein or gene information. Novel in this work, we take advantage of both and focus on protein evolutionary information by using estimated selective pressures at the codon level. Here we introduce a new method (SeqProfCod) to predict the likelihood that a given protein variant is associated with human disease or not. Our method relies on a support vector machine (SVM) classifier trained using three sources of information: protein sequence, multiple protein sequence alignments, and the estimation of selective pressure at the codon level. SeqProfCod has been benchmarked with a large dataset of 8,987 single point mutations from 1,434 human proteins from SWISS-PROT. It achieves 82\% overall accuracy and a correlation coefficient of 0.59, indicating that the estimation of the selective pressure helps in predicting the functional impact of single-point mutations. Moreover, this study demonstrates the synergic effect of combining two sources of information for predicting the functional effects of protein variants: protein sequence/profile-based information and the evolutionary estimation of the selective pressures at the codon level. The results of large-scale application of SeqProfCod over all annotated point mutations in SWISS-PROT (available for download at http://sgu.bioinfo.cipf.es/services/Omidios/; last accessed: 24 August 2007), could be used to support clinical studies.</p>},
	keywords = {Algorithms, Codon, Computational Biology, Databases, Protein, DNA Mutational Analysis, Evolution, Molecular, Genetic Predisposition to Disease, Genetic Variation, Genome, Human, Humans, Iduronic Acid, Point Mutation, Polymorphism, Single Nucleotide, Proteins, Tumor Suppressor Protein p53},
	issn = {1098-1004},
	doi = {10.1002/humu.20628},
	author = {Capriotti, Emidio and Arbiza, Leonardo and Casadio, Rita and Dopazo, Joaquin and Dopazo, Hern{\'a}n and Marti-Renom, Marc A}
}
