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Journal Article
Melo F, Marti-Renom MA. Accuracy of sequence alignment and fold assessment using reduced amino acid alphabets. Proteins. 2006;63:986-95. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16506243.
Tárraga J, Arnau V, Martinez H, et al. Acceleration of short and long DNA read mapping without loss of accuracy using suffix array. Bioinformatics (Oxford, England). 2014;30:3396-3398. doi:10.1093/bioinformatics/btu553.
Tárraga J, Arnau V, Martinez H, et al. Acceleration of short and long DNA read mapping without loss of accuracy using suffix array. Bioinformatics (Oxford, England). 2014;30:3396-3398. doi:10.1093/bioinformatics/btu553.
Tárraga J, Arnau V, Martinez H, et al. Acceleration of short and long DNA read mapping without loss of accuracy using suffix array. Bioinformatics (Oxford, England). 2014;30:3396-3398. doi:10.1093/bioinformatics/btu553.
Dopazo J, Amadoz A, Bleda M, et al. 267 Spanish exomes reveal population-specific differences in disease-related genetic variation. Molecular biology and evolution. 2016. doi:10.1093/molbev/msw005.
Dopazo J, Amadoz A, Bleda M, et al. 267 Spanish exomes reveal population-specific differences in disease-related genetic variation. Molecular biology and evolution. 2016. doi:10.1093/molbev/msw005.
Conference Paper
Conde L, Mateos A, Herrero J, Dopazo J. Unsupervised reduction of the dimensionality followed by supervised learning with a perceptron improves the classification of conditions in DNA microarray gene expression data. In: Neural Networks for Signal Processing XII. 2002 IEEE Signal Processing Society WorkshopProceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing. Neural Networks for Signal Processing XII. 2002 IEEE Signal Processing Society WorkshopProceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing. Martigny, Switzerland: IEEE; 2002. doi:10.1109/NNSP.2002.1030019.
Silbiger V, Luchessi A, Hirata R, et al. Peripheral blood cells transcriptome to study new biomarkers for myocardial infarction follow up. In: ; 2009.
Gonzalez CY, Bleda M, Salavert F, Sánchez R, Dopazo J, Medina I. Multicore and Cloud-based Solutions for Genomic Variant Analysis. In: Proceedings of the 18th International Conference on Parallel Processing Workshops. Proceedings of the 18th International Conference on Parallel Processing Workshops. Berlin, Heidelberg: Springer-Verlag; 2013. doi:10.1007/978-3-642-36949-0_30.
Book Chapter
Mateos A, Herrero J, Dopazo J. Using perceptrons for supervised classification of DNA microarray samples: obtaining the optimal level of information and finding differentially expressed genes. In: ICANN 2002, LNCS 2415. ICANN 2002, LNCS 2415. J.R. Dorronsoro; 2002:577-582.
Al-Shahrour F, Herrero J, Mateos A, Santoyo J, Díaz-Uriarte R, Dopazo J. Using Gene Ontology on genome-scale studies to find significant associations of biologically relevant terms to group of genes. In: Neural Networks for Signal Processing XIII. Neural Networks for Signal Processing XIII. New York, USA: IEEE Press; 2003:43-52.
Mateos A, Herrero J, Tamames J, Dopazo J. Supervised Neural Networks For Clustering Conditions In DNA Array Data After Reducing Noise By Clustering Gene Expression Profiles. In: Microarray data analysis II. Microarray data analysis II. Kluwer Academic; 2002:91-103.
Marti-Renom MA, Capriotti E, Shindyalov I, Bourne P. Structural Comparison and Alignment. In: Structural Bioinformatics. 2ndnd ed. Structural Bioinformatics. New Jersey. USA: Wiley-Blackwell; 2009. Available at: http://www.amazon.com/gp/product/0470181052/.
Minguez P, Dopazo J. Protein Interactions for Functional Genomics. In: Li X-L, Ng S-K, eds. Biological Data Mining in Protein Interaction Networks. Biological Data Mining in Protein Interaction Networks. Hershey, USA: Idea Group Inc (IGI); 2009:223-238. Available at: http://books.google.es/books?id=pNyCy5GsqtkC.
Montaner D, Al-Shahrour F, Dopazo J. New Trends in the Analysis of Functional Genomic Data. In: Progress in Industrial Mathematics at ECMI 2006.Vol 12. Progress in Industrial Mathematics at ECMI 2006. Berlin: Springer; 2007:576-580. doi:10.1007/978-3-540-71992-2_94.
Garcia-Garcia F, Montaner D. Docencia en Estadística: Experiencias de Innovación. In: III Jornadas de Intercambio de Experiencias de Innovación Educativa en Estadística.Vol 1. III Jornadas de Intercambio de Experiencias de Innovación Educativa en Estadística.; 2013:201-210.
Gabaldón T, Gil R, Peretó J, Latorre A, Moya A. The core of a minimal gene set: insights from natural reduced genomes. In: Protocells: Bridging nonliving and living matter. Protocells: Bridging nonliving and living matter. USA: The MIT Press; 2008:347-366.
Capriotti E, Marti-Renom MA. Assessment of protein structure predictions. In: Computational Structural Biology. Computational Structural Biology. New Jersey, USA: World Scientific Publishing Company; 2008. Available at: http://www.amazon.com/dp/9812778772/.
Book
Mateos A, Herrero J, Tamames J, Dopazo J. Methods of Microarray Data Analysis IISupervised Neural Networks for Clustering Conditions in DNA Array Data After Reducing Noise by Clustering Gene Expression Profiles. (Lin SM, Johnson KF, eds.). Boston: Kluwer Academic Publishers; 2002:91 - 103. doi:10.1007/b11298210.1007/0-306-47598-7_7.