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2025
Martínez-Nava GAngélica, Altamirano-Molina E, Vázquez-Mellado J, et al. Metatranscriptomic analysis reveals gut microbiome bacterial genes in pyruvate and amino acid metabolism associated with hyperuricemia and gout in humans. Sci Rep. 2025;15(1):9981. doi:10.1038/s41598-025-93899-1.
2024
Pérez-Gutiérrez AM, Carmona R, Loucera C, et al. Mutational landscape of risk variants in comorbid depression and obesity: a next-generation sequencing approach. Mol Psychiatry. 2024. doi:10.1038/s41380-024-02609-2.
2023
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.
2022
Cruz R, de Almeida SDiz-, Heredia MLópez, et al. Novel genes and sex differences in COVID-19 severity. Hum Mol Genet. 2022. doi:10.1093/hmg/ddac132.
Cruz R, de Almeida SDiz-, Heredia MLópez, et al. Novel genes and sex differences in COVID-19 severity. Hum Mol Genet. 2022. doi:10.1093/hmg/ddac132.
2021
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.
Heath AP, Ferretti V, Agrawal S, et al. The NCI Genomic Data Commons. Nature Genetics. 2021. doi:10.1038/s41588-021-00791-5.
Heath AP, Ferretti V, Agrawal S, et al. The NCI Genomic Data Commons. Nature Genetics. 2021. doi:10.1038/s41588-021-00791-5.
Heath AP, Ferretti V, Agrawal S, et al. The NCI Genomic Data Commons. Nature Genetics. 2021. doi:10.1038/s41588-021-00791-5.
Heath AP, Ferretti V, Agrawal S, et al. The NCI Genomic Data Commons. Nature Genetics. 2021. doi:10.1038/s41588-021-00791-5.
Heath AP, Ferretti V, Agrawal S, et al. The NCI Genomic Data Commons. Nature Genetics. 2021. doi:10.1038/s41588-021-00791-5.
Mirzayi C, Renson A, Zohra F, et al. Reporting guidelines for human microbiome research: the STORMS checklist. Nat Med. 2021;27(11):1885-1892. doi:10.1038/s41591-021-01552-x.
Mirzayi C, Renson A, Zohra F, et al. Reporting guidelines for human microbiome research: the STORMS checklist. Nat Med. 2021;27(11):1885-1892. doi:10.1038/s41591-021-01552-x.
Mirzayi C, Renson A, Zohra F, et al. Reporting guidelines for human microbiome research: the STORMS checklist. Nat Med. 2021;27(11):1885-1892. doi:10.1038/s41591-021-01552-x.
Mirzayi C, Renson A, Zohra F, et al. Reporting guidelines for human microbiome research: the STORMS checklist. Nat Med. 2021;27(11):1885-1892. doi:10.1038/s41591-021-01552-x.
Mirzayi C, Renson A, Zohra F, et al. Reporting guidelines for human microbiome research: the STORMS checklist. Nat Med. 2021;27(11):1885-1892. doi:10.1038/s41591-021-01552-x.
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.
Zhang Z, Hernandez K, Savage J, et al. Uniform genomic data analysis in the NCI Genomic Data CommonsAbstract. Nature Communications. 2021;12(1). doi:10.1038/s41467-021-21254-9.
2019
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.
2016
Corton M, Avila-Fernández A, Campello L, et al. Identification of the Photoreceptor Transcriptional Co-Repressor SAMD11 as Novel Cause of Autosomal Recessive Retinitis Pigmentosa. Sci Rep. 2016;6:35370. doi:10.1038/srep35370.
2015
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.
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.
2010
Shi L, Campbell G, Jones WD, et al. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nature biotechnology. 2010;28:827-38. Available at: http://www.nature.com/nbt/journal/v28/n8/full/nbt.1665.html.
Shi L, Campbell G, Jones WD, et al. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nature biotechnology. 2010;28:827-38. Available at: http://www.nature.com/nbt/journal/v28/n8/full/nbt.1665.html.
Shi L, Campbell G, Jones WD, et al. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nature biotechnology. 2010;28:827-38. Available at: http://www.nature.com/nbt/journal/v28/n8/full/nbt.1665.html.