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Nueda MJosé, Sebastián P, Tarazona S, et al. Functional assessment of time course microarray data. BMC Bioinformatics. 2009;10 Suppl 6:S9. doi:10.1186/1471-2105-10-S6-S9.
Nueda MJ, Ferrer A, Conesa A. ARSyN: a method for the identification and removal of systematic noise in multifactorial time course microarray experiments. Biostatistics (Oxford, England). 2011.
Nueda MJ, Conesa A, Westerhuis JA, et al. Discovering gene expression patterns in time course microarray experiments by ANOVA-SCA. Bioinformatics. 2007;23:1792-800. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17519250.
Nobre LS, Al-Shahrour F, Dopazo J, Saraiva LM. Exploring the antimicrobial action of a carbon monoxide-releasing compound through whole-genome transcription profiling of Escherichia coli. Microbiology (Reading). 2009;155(Pt 3):813-824. doi:10.1099/mic.0.023911-0.
Nobre LS, Al-Shahrour F, Dopazo J, Saraiva LM. Exploring the antimicrobial action of a carbon monoxide-releasing compound through whole-genome transcription profiling of Escherichia coli. Microbiology. 2009;155:813-24. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=19246752.
Niarakis A, Ostaszewski M, Mazein A, et al. Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches. Front Immunol. 2024;14:1282859. doi:10.3389/fimmu.2023.1282859.
Németh A, Conesa A, Santoyo-López J, et al. Initial genomics of the human nucleolus. PLoS genetics. 2010;6:e1000889. doi:10.1371/journal.pgen.1000889.
Negredo A, Palacios G, Vázquez-Morón S, et al. Discovery of an ebolavirus-like filovirus in europe. PLoS pathogens. 2011;7:e1002304.
M
Munro SA, Lund SP, P Pine S, et al. Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures. Nature communications. 2014;5:5125. doi:10.1038/ncomms6125.
Moura DS, Peña-Chilet M, Varela JAntonio Co, et al. A DNA damage repair gene-associated signature predicts responses of patients with advanced soft-tissue sarcoma to treatment with trabectedin. Mol Oncol. 2021;15(12):3691-3705. doi:10.1002/1878-0261.12996.
Moschen S, Di Rienzo JA, Higgins J, et al. Integration of transcriptomic and metabolic data reveals hub transcription factors involved in drought stress response in sunflower (Helianthus annuus L.). Plant Mol Biol. 2017;94(4-5):549-564. doi:10.1007/s11103-017-0625-5.
Moschen S, Luoni SBengoa, Di Rienzo JA, et al. Integrating transcriptomic and metabolomic analysis to understand natural leaf senescence in sunflower. Plant Biotechnol J. 2016;14(2):719-34. doi:10.1111/pbi.12422.
Moreno-Manzano V, Rodríguez-Jiménez FJ, Aceña-Bonilla JL, et al. FM19G11, a new hypoxia-inducible factor (HIF) modulator, affects stem cell differentiation status. The Journal of biological chemistry. 2010;285:1333-42.
Montero-Conde C, Martin-Campos JM, Lerma E, et al. Molecular profiling related to poor prognosis in thyroid carcinoma. Combining gene expression data and biological information. Oncogene. 2008;27:1554-61. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17873908.
Montero-Conde C, Martín-Campos JM, Lerma E, et al. Molecular profiling related to poor prognosis in thyroid carcinoma. Combining gene expression data and biological information. Oncogene. 2008;27(11):1554-61. doi:10.1038/sj.onc.1210792.
Montaner D, Minguez P, Al-Shahrour F, Dopazo J. Gene set internal coherence in the context of functional profiling. BMC Genomics. 2009;10:197. doi:10.1186/1471-2164-10-197.
Montaner D, Tarraga J, Huerta-Cepas J, et al. Next station in microarray data analysis: GEPAS. Nucleic Acids Res. 2006;34:W486-91. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16845056.
Montaner D, Dopazo J. Multidimensional gene set analysis of genomic data. PLoS One. 2010;5(4):e10348. doi:10.1371/journal.pone.0010348.
Montaner D, Al-Shahrour F, Dopazo J. New Trends in the Analysis of Functional Genomic Data. In: Progress in Industrial Mathematics at ECMI 2006.Vol 12. Progress in Industrial Mathematics at ECMI 2006. Berlin: Springer; 2007:576-580. doi:10.1007/978-3-540-71992-2_94.
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.
Mirkovic N, Marti-Renom MA, Weber BL, Sali A, Monteiro AN. Structure-based assessment of missense mutations in human BRCA1: implications for breast and ovarian cancer predisposition. Cancer Res. 2004;64:3790-7. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15172985.
Minguez P, Al-Shahrour F, Dopazo J. A function-centric approach to the biological interpretation of microarray time-series. Genome Inform. 2006;17:57-66.
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.
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, Dopazo J. Functional genomics and networks: new approaches in the extraction of complex gene modules. Expert Rev Proteomics. 2010;7(1):55-63. doi:10.1586/epr.09.103.