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Calzada D, Aguerri M, Baos S, et al. Therapeutic targets for olive pollen allergy defined by gene markers modulated by Ole e 1-derived peptides. Molecular immunology. 2015;64:252-61. doi:10.1016/j.molimm.2014.12.002.
Méndez-Salazar EOrlando, Vázquez-Mellado J, Casimiro-Soriguer CS, et al. Taxonomic variations in the gut microbiome of gout patients with and without tophi might have a functional impact on urate metabolism. Mol Med. 2021;27(1):50. doi:10.1186/s10020-021-00311-5.
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Mateos A, Dopazo J, Jansen R, Tu Y, Gerstein M, Stolovitzky G. Systematic learning of gene functional classes from DNA array expression data by using multilayer perceptrons. Genome Res. 2002;12:1703-15. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12421757.
Cuenca-Bono B, García-Molinero V, Pascual-García P, et al. SUS1 introns are required for efficient mRNA nuclear export in yeast. Nucleic acids research. 2011;39:8599-611.
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.
Razzoli M, Frontini A, Gurney A, et al. Stress-induced activation of brown adipose tissue prevents obesity in conditions of low adaptive thermogenesis. Mol Metab. 2016;5(1):19-33. doi:10.1016/j.molmet.2015.10.005.
Birmingham A, Selfors LM, Forster T, et al. Statistical methods for analysis of high-throughput RNA interference screens. Nature Methods. 2009;6:569 - 575. Available at: http://dx.doi.org/10.1038/nmeth.1351.
Levin AM, de Vries RP, Conesa A, et al. Spatial differentiation in the vegetative mycelium of Aspergillus niger. Eukaryot Cell. 2007;6:2311-22. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17951513.
Levin AM, de Vries RP, Conesa A, et al. Spatial differentiation in the vegetative mycelium of Aspergillus niger. Eukaryot Cell. 2007;6:2311-22. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17951513.
De Baets G, Van Durme J, Reumers J, et al. SNPeffect 4.0: on-line prediction of molecular and structural effects of protein-coding variants. Nucleic Acids Res. 2012;40(Database issue):D935-9. doi:10.1093/nar/gkr996.
De Baets G, Van Durme J, Reumers J, et al. SNPeffect 4.0: on-line prediction of molecular and structural effects of protein-coding variants. Nucleic Acids Res. 2012;40(Database issue):D935-9. doi:10.1093/nar/gkr996.
Saar K, Beck A, Bihoreau MT, et al. SNP and haplotype mapping for genetic analysis in the rat. Nat Genet. 2008;40:560-6. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18443594.
Saar K, Beck A, Bihoreau M-T, et al. SNP and haplotype mapping for genetic analysis in the rat. Nat Genet. 2008;40(5):560-6. doi:10.1038/ng.124.
Saar K, Beck A, Bihoreau MT, et al. SNP and haplotype mapping for genetic analysis in the rat. Nat Genet. 2008;40:560-6. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18443594.
Saar K, Beck A, Bihoreau M-T, et al. SNP and haplotype mapping for genetic analysis in the rat. Nat Genet. 2008;40(5):560-6. doi:10.1038/ng.124.
Minguez P, Götz S, Montaner D, Al-Shahrour F, Dopazo J. SNOW, a web-based tool for the statistical analysis of protein-protein interaction networks. Nucleic Acids Res. 2009;37(Web Server issue):W109-14. doi:10.1093/nar/gkp402.
Minguez P, Gotz S, Montaner D, Al-Shahrour F, Dopazo J. SNOW, a web-based tool for the statistical analysis of protein-protein interaction networks. Nucl. Acids Res. 2009;37:W109-114. doi:10.1093/nar/gkp402.
López-López D, Loucera C, Carmona R, et al. SMN1 copy-number and sequence variant analysis from next-generation sequencing data. Hum Mutat. 2020;41(12):2073-2077. doi:10.1002/humu.24120.
Gundogdu P, Alamo I, Nepomuceno-Chamorro IA, Dopazo J, Loucera C. SigPrimedNet: A Signaling-Informed Neural Network for scRNA-seq Annotation of Known and Unknown Cell Types. Biology (Basel). 2023;12(4). doi:10.3390/biology12040579.
Lüke L, Vicens A, Serra F, et al. Sexual selection halts the relaxation of protamine 2 among rodents. PloS one. 2011;6:e29247. doi:10.1371/journal.pone.0029247.
Martin-Coello J, Dopazo H, Arbiza L, Ausio J, Roldan ER, Gomendio M. Sexual selection drives weak positive selection in protamine genes and high promoter divergence, enhancing sperm competitiveness. Proc Biol Sci. 2009. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=19364735.
Puchades-Carrasco L, Jantus-Lewintre E, Pérez-Rambla C, et al. Serum metabolomic profiling facilitates the non-invasive identification of metabolic biomarkers associated with the onset and progression of non-small cell lung cancer. Oncotarget. 2016;7(11):12904-16. doi:10.18632/oncotarget.7354.
Nueda MJosé, Carbonell J, Medina I, Dopazo J, Conesa A. Serial Expression Analysis: a web tool for the analysis of serial gene expression data. Nucleic Acids Res. 2010;38(Web Server issue):W239-45. doi:10.1093/nar/gkq488.
Larriba E, Jaime MDLA, Carbonell-Caballero J, et al. Sequencing and functional analysis of the genome of a nematode egg-parasitic fungus, Pochonia chlamydosporia. Fungal Genet Biol. 2014;65:69-80. doi:10.1016/j.fgb.2014.02.002.
Arbiza L, Duchi S, Montaner D, et al. Selective pressures at a codon-level predict deleterious mutations in human disease genes. J Mol Biol. 2006;358:1390-404. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16584746.