Home

Publications

Export 493 results:
[ Author(Asc)] Title Type Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
D
Dopazo H, Gordon MB, Perazzo R, Risau-Gusman S. A model for the interaction of learning and evolution. Bull Math Biol. 2001;63:117-34. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11146879.
Dopazo H. Selective Constraints and Human Disease Genes: Evolutionary and Bioinformatic Approaches. In: Encyclopedia of Life Science. Encyclopedia of Life Science. UK: John Wiley & Sons, Ltd.; 2008. doi:10.1002/9780470015902.a0020762.
Dopazo H. Bioinformática, Genómica y Evolución. Una alianza estratégica para la biología de este siglo. Ciencia Hoy. 2009;19:88-93.
Dopazo J. Genomics and transcriptomics in drug discovery. Drug discovery today. 2014;19:126-32. doi:10.1016/j.drudis.2013.06.003.
Dopazo J, Al-Shahrour F. Expression and microarrays. Methods Mol Biol. 2008;453:245-55. doi:10.1007/978-1-60327-429-6_12.
Dopazo J, Erten C. Graph-theoretical comparison of normal and tumor networks in identifying BRCA genes. BMC Systems Biology. 2017;11(1). doi:10.1186/s12918-017-0495-0.
Dopazo J, Aloy P. Discovery and hypothesis generation through bioinformatics. Genome Biol. 2006;7:307. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16522224.
Dopazo H, Santoyo J, Dopazo J. Phylogenomics and the number of characters required for obtaining an accurate phylogeny of eukaryote model species. Bioinformatics. 2004;20 Suppl 1:i116-21. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15262789.
Dopazo J. Bioinformatics and cancer: an essential alliance. Clin Transl Oncol. 2006;8:409-15. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16790393.
Dopazo H, Gordon MB, Perazzo R, Risau-Gusman S. A model for the emergence of adaptive subsystems. Bull Math Biol. 2003;65:27-56. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12597115.
Dopazo H, Dopazo J. Genome-scale evidence of the nematode-arthropod clade. Genome Biol. 2005;6:R41. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15892869.
Dopazo J, Mendoza A, Herrero J, et al. Annotated draft genomic sequence from a Streptococcus pneumoniae type 19F clinical isolate. Microb Drug Resist. 2001;7:99-125. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11442348.
Dopazo J. Functional profiling methods in cancer. In: Grützmann R, Pilarsky C, eds. Methods in molecular biology (Clifton, N.J.).Vol 576. Methods in molecular biology (Clifton, N.J.).; 2010:363-74.
Dopazo H. La clasificación de los organismos. In: Invitación a la Biología. Invitación a la Biología. Buenos Aires: Curtis, Barnes, Schnek & Flores. 2da, Editorial Medica Panamericana; 2006.
Dopazo J. Formulating and testing hypotheses in functional genomics. Artif Intell Med. 2009;45:97-107. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18789659.
Dopazo J. Microarray Data Processing And Analysis. In: Microarray data analysis II. Microarray data analysis II. Kluwer Academic; 2002:43-63.
Dopazo J, Erten C. Graph-theoretical comparison of normal and tumor networks in identifying BRCA genes. BMC Syst Biol. 2017;11(1):110. doi:10.1186/s12918-017-0495-0.
Dopazo J. Functional interpretation of microarray experiments. OMICS. 2006;10:398-410. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17069516.
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. Clustering - Class discovery in the post-genomic era. In: Fundamentals of data mining in genomics and proteomics. Fundamentals of data mining in genomics and proteomics. New York, USA: Springer-Verlag, W. Dubitzky, M. Granzow and D.P. Berrar; 2007.
Dopazo J, Maya-Miles D, García F, et al. Implementing Personalized Medicine in COVID-19 in Andalusia: An Opportunity to Transform the Healthcare System. J Pers Med. 2021;11(6). doi:10.3390/jpm11060475.
Díez-Fuertes F, De La Torre-Tarazona HE, Calonge E, et al. Association of a single nucleotide polymorphism in the ubxn6 gene with long-term non-progression phenotype in HIV-positive individuals. Clin Microbiol Infect. 2020;26(1):107-114. doi:10.1016/j.cmi.2019.05.015.
Díaz-Uriarte R, Al-Shahrour F, Dopazo J. Use of GO Terms to Understand the Biological Significance of Microarray Differential Gene Expression Data. In: Microarray data analysis III. Microarray data analysis III. Kluwer Academic, K. F. Johnson and S. M. Lin; 2003:233-247.
Desoignies N, Carbonell J, Moreau J-S, Conesa A, Dopazo J, Legrève A. Molecular interactions between sugar beet and Polymyxa betae during its life cycle. Annals of Applied Biology. 2014;164:244–256. doi:10.1111/aab.12095.
del Pozo MGonzález-, Méndez-Vidal C, Santoyo-López J, et al. Deciphering intrafamilial phenotypic variability by exome sequencing in a Bardet–Biedl family. Molecular Genetics & Genomic Medicine. 2014;2:124-133. doi:10.1002/mgg3.50.