Publications

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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.
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.
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.
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, 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.
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.
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.
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, Dopazo J. Assessing the biological significance of gene expression signatures and co-expression modules by studying their network properties. PloS one. 2011;6:e17474. doi:doi:10.1371/journal.pone.0017474.
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, Letunic I, Parca L, et al. PTMcode v2: a resource for functional associations of post-translational modifications within and between proteins. Nucleic Acids Res. 2015;43(Database issue):D494-502. doi:10.1093/nar/gku1081.
Minguez P, Al-Shahrour F, Montaner D, Dopazo J. Functional profiling of microarray experiments using text-mining derived bioentities. Bioinformatics. 2007;23(22):3098-9. doi:10.1093/bioinformatics/btm445.
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, 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.
Minguez P, Al-Shahrour F, Montaner D, Dopazo J. Functional profiling of microarray experiments using text-mining derived bioentities. Bioinformatics. 2007;23:3098-9. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17855415.
Minguez P, Dopazo J. Protein Interactions for Functional Genomics. In: Li X-L, Ng S-K, eds. Biological Data Mining in Protein Interaction Networks. Biological Data Mining in Protein Interaction Networks. Hershey, USA: Idea Group Inc (IGI); 2009:223-238. Available at: http://books.google.es/books?id=pNyCy5GsqtkC.
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.
Milne RL, Ribas G, Gonzalez-Neira A, et al. ERCC4 associated with breast cancer risk: a two-stage case-control study using high-throughput genotyping. Cancer Res. 2006;66:9420-7. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17018596.
Méndez-Vidal C, Bravo-Gil N, del Pozo MGonzález-, et al. Novel RP1 mutations and a recurrent BBS1 variant explain the co-existence of two distinct retinal phenotypes in the same pedigree. BMC Genet. 2014;15:143. doi:10.1186/s12863-014-0143-2.
Méndez-Vidal C, del Pozo MGonzález-, Vela-Boza A, et al. Whole-exome sequencing identifies novel compound heterozygous mutations in USH2A in Spanish patients with autosomal recessive retinitis pigmentosa. Molecular vision. 2013;19:2187-95. Available at: http://www.molvis.org/molvis/v19/2187/.
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.
Melo F, Marti-Renom MA. Accuracy of sequence alignment and fold assessment using reduced amino acid alphabets. Proteins. 2006;63:986-95. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16506243.