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 
F
Fernández RMa, Bleda M, Núñez-Torres R, et al. Four new loci associations discovered by pathway-based and network analyses of the genome-wide variability profile of Hirschsprung's disease. Orphanet J Rare Dis. 2012;7:103. doi:10.1186/1750-1172-7-103.
Fernandez P, Soria M, Blesa D, et al. Development, Characterization and Experimental Validation of a Cultivated Sunflower (Helianthus annuus L.) Gene Expression Oligonucleotide Microarray. PloS one. 2012;7:e45899. doi:10.1371/journal.pone.0045899.
Fernández RM, Bleda M, Luzón-Toro B, et al. Pathways systematically associated to Hirschsprung’s disease. Orphanet journal of rare diseases. 2013;8:187. doi:10.1186/1750-1172-8-187.
Fernández RM, Bleda M, Luzón-Toro B, et al. Pathways systematically associated to Hirschsprung's disease. Orphanet J Rare Dis. 2013;8:187. doi:10.1186/1750-1172-8-187.
Falco MM, Peña-Chilet M, Loucera C, Hidalgo MR, Dopazo J. Mechanistic models of signaling pathways deconvolute the glioblastoma single-cell functional landscapeAbstract. NAR Cancer. 2020;2(2). doi:10.1093/narcan/zcaa011.
Falco MM, Bleda M, Carbonell-Caballero J, Dopazo J. The pan-cancer pathological regulatory landscape. Scientific reports. 2016;6:39709. doi:10.1038/srep39709.
Falco MM, Bleda M, Carbonell-Caballero J, Dopazo J. The pan-cancer pathological regulatory landscape. Scientific Reports. 2016;6(1). doi:10.1038/srep39709.
E
Eyrich VA, Marti-Renom MA, Przybylski D, et al. EVA: continuous automatic evaluation of protein structure prediction servers. Bioinformatics. 2001;17:1242-3. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11751240.
Ewing AD, Houlahan KE, Hu Y, et al. Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection. participants ICGC-TCGADREAMSoma, Xi L, Dewal N, et al., eds. Nature methods. 2015. doi:10.1038/nmeth.3407.
Eswar N, John B, Mirkovic N, et al. Tools for comparative protein structure modeling and analysis. Nucleic Acids Res. 2003;31:3375-80. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12824331.
Eswar N, Webb B, Marti-Renom MA, et al. Comparative protein structure modeling using Modeller. Curr Protoc Bioinformatics. 2006;Chapter 5:Unit 5 6. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18428767.
Esteban-Medina M, Peña-Chilet M, Loucera C, Dopazo J. Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models. BMC Bioinformatics. 2019;20(1):370. doi:10.1186/s12859-019-2969-0.
Esteban-Medina M, Loucera C, Rian K, et al. The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery. J Transl Med. 2024;22(1):139. doi:10.1186/s12967-024-04911-7.
Espadaler J, Eswar N, Querol E, et al. Prediction of enzyme function by combining sequence similarity and protein interactions. BMC Bioinformatics. 2008;9:249. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18505562.
Espadaler J, Aragues R, Eswar N, et al. Detecting remotely related proteins by their interactions and sequence similarity. Proc Natl Acad Sci U S A. 2005;102:7151-6. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15883372.
Eramian D, Shen MY, Devos D, Melo F, Sali A, Marti-Renom MA. A composite score for predicting errors in protein structure models. Protein Sci. 2006;15:1653-66. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16751606.
Elena SF, Dopazo J, de la Pena M, Flores R, Diener TO, Moya A. Phylogenetic analysis of viroid and viroid-like satellite RNAs from plants: a reassessment. J Mol Evol. 2001;53:155-9. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11479686.
Eduati F, Mangravite LM, Wang T, et al. Prediction of human population responses to toxic compounds by a collaborative competition. Nature biotechnology. 2015. doi:10.1038/nbt.3299.
D
Durban J, Juárez P, Angulo Y, et al. Profiling the venom gland transcriptomes of Costa Rican snakes by 454 pyrosequencing. BMC genomics. 2011;12:259.
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. 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. 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, 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, Al-Shahrour F. Functional annotation of microarray experiments. In: Microarray Technology Through Applications. Microarray Technology Through Applications. New York, USA: Taylor & Francis, F. Falciani; 2007.
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.