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Fourati S, Talla A, Mahmoudian M, et al. A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection. Nature Communications. 2018;9(1). doi:10.1038/s41467-018-06735-8.
Cubuk C, Loucera C, Peña-Chilet M, Dopazo J. Crosstalk between Metabolite Production and Signaling Activity in Breast Cancer. Int J Mol Sci. 2023;24(8). doi:10.3390/ijms24087450.
Ostaszewski M, Mazein A, Gillespie ME, et al. COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms. Sci Data. 2020;7(1):136. doi:10.1038/s41597-020-0477-8.
Ostaszewski M, Niarakis A, Mazein A, et al. COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms. Mol Syst Biol. 2021;17(10):e10387. doi:10.15252/msb.202110387.
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.
Horcajadas JA, Minguez P, Dopazo J, et al. Controlled ovarian stimulation induces a functional genomic delay of the endometrium with potential clinical implications. J Clin Endocrinol Metab. 2008;93:4500-10. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18697870.
Martinez H, Tárraga J, Medina I, et al. Concurrent and Accurate Short Read Mapping on Multicore Processors. IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM. 2015;12:995-1007. doi:10.1109/TCBB.2015.2392077.
Gabaldón T. Computational approaches for the prediction of protein function in the mitochondrion. Am J Physiol Cell Physiol. 2006;291:C1121-8. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16870830.
F Carmona J, Davalos V, Vidal E, et al. A Comprehensive DNA Methylation Profile of Epithelial-to-Mesenchymal Transition. Cancer research. 2014;74:5608–19. doi:10.1158/0008-5472.CAN-13-3659.
Martorell-Marugán J, López-Domínguez R, García-Moreno A, et al. A comprehensive database for integrated analysis of omics data in autoimmune diseases. BMC Bioinformatics. 2021;22(1):343. doi:10.1186/s12859-021-04268-4.
Su Z, Labaj PP, , et al. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nature biotechnology. 2014;32:903–914. doi:10.1038/nbt.2957.
Núñez-Torres R, Pita G, Peña-Chilet M, et al. A Comprehensive Analysis of 21 Actionable Pharmacogenes in the Spanish Population: From Genetic Characterisation to Clinical Impact. Pharmaceutics. 2023;15(4). doi:10.3390/pharmaceutics15041286.
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.
Amadoz A, Hidalgo MR, Cubuk C, Carbonell-Caballero J, Dopazo J. A comparison of mechanistic signaling pathway activity analysis methods. Brief Bioinform. 2019;20(5):1655-1668. doi:10.1093/bib/bby040.
Martin MJ, Herrero J, Mateos A, Dopazo J. Comparing bacterial genomes through conservation profiles. Genome Res. 2003;13:991-8. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12695324.
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.
Gabaldón T. Comparative genomics-based prediction of protein function. In: Methods in Molecular Biology.Vol 439. Methods in Molecular Biology. M. Starkey and R. Elaswarapu, Humana press; 2008. Available at: http://www.springerprotocols.com/Abstract/doi/10.1007/978-1-59745-188-8_26.
de la Rosa LRodríguez, Sánchez-Calderón H, Contreras J, et al. Comparative gene expression study of the vestibular organ of the Igf1 deficient mouse using whole-transcript arrays. Hearing research. 2015. doi:10.1016/j.heares.2015.08.016.
Menden MP, Wang D, Mason MJ, et al. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. Nat Commun. 2019;10(1):2674. doi:10.1038/s41467-019-09799-2.
Yang M, Petralia F, Li Z, et al. Community Assessment of the Predictability of Cancer Protein and Phosphoprotein Levels from Genomics and Transcriptomics. Cell Syst. 2020;11(2):186-195.e9. doi:10.1016/j.cels.2020.06.013.
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.
Herrero J, Dopazo J. Combining hierarchical clustering and self-organizing maps for exploratory analysis of gene expression patterns. J Proteome Res. 2002;1:467-70. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12645919.
Huynen MA, Spronk CA, Gabaldón T, Snel B. Combining data from genomes, Y2H and 3D structure indicates that BolA is a reductase interacting with a glutaredoxin. FEBS Lett. 2005;579:591-6. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15670813.
de Castro-Miró M, Pomares E, Lorés-Motta L, et al. Combined genetic and high-throughput strategies for molecular diagnosis of inherited retinal dystrophies. PloS one. 2014;9:e88410. doi:10.1371/journal.pone.0088410.
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.