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C
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.
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.
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.
Sanghez V, Cubuk C, Sebastián-Leon P, et al. Chronic subordination stress selectively downregulates the insulin signaling pathway in liver and skeletal muscle but not in adipose tissue of male mice. Stress (Amsterdam, Netherlands). 2016:1-11. doi:10.3109/10253890.2016.1151491.
Javierre BM, Fernandez AF, Richter J, et al. Changes in the pattern of DNA methylation associate with twin discordance in systemic lupus erythematosus. Genome research. 2010;20:170-9.
Gundogdu P, Payá-Milans M, Alamo-Alvarez I, Nepomuceno-Chamorro IA, Dopazo J, Loucera C. Cell-Level Pathway Scoring Comparison with a Biologically Constrained Variational Autoencoder. Cham: Springer Nature Switzerland; 2023:62 - 77. doi:10.1007/978-3-031-42697-110.1007/978-3-031-42697-1_5.
Bleda M, Tárraga J, De Maria A, et al. CellBase, a comprehensive collection of RESTful web services for retrieving relevant biological information from heterogeneous sources. Nucleic acids research. 2012;40:W609-14. doi:10.1093/nar/gks575.
B
Alonso R, Salavert F, Garcia-Garcia F, et al. Babelomics 5.0: functional interpretation for new generations of genomic data. Nucleic acids research. 2015;43:W117-W121. doi:10.1093/nar/gkv384.
A
Gonzalez S, Clavijo B, Rivarola M, et al. ATGC transcriptomics: a web-based application to integrate, explore and analyze de novo transcriptomic data. BMC Bioinformatics. 2017;18(1):121. doi:10.1186/s12859-017-1494-2.
Loucera C, Perez-Florido J, Casimiro-Soriguer CS, et al. Assessing the Impact of SARS-CoV-2 Lineages and Mutations on Patient Survival. Viruses. 2022;14(9). doi:10.3390/v14091893.
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.
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.
Casimiro-Soriguer CS, Loucera C, Florido JPerez, López-López D, Dopazo J. Antibiotic resistance and metabolic profiles as functional biomarkers that accurately predict the geographic origin of city metagenomics samples. Biol Direct. 2019;14(1):15. doi:10.1186/s13062-019-0246-9.
Trevino V, Tadesse MG, Vannucci M, et al. Analysis of normal-tumour tissue interaction in tumours: prediction of prostate cancer features from the molecular profile of adjacent normal cells. PloS one. 2011;6:e16492.
Iglesias JManuel, Leis O, Ruiz EPérez, et al. The Activation of the Sox2 RR2 Pluripotency Transcriptional Reporter in Human Breast Cancer Cell Lines is Dynamic and Labels Cells with Higher Tumorigenic Potential. Front Oncol. 2014;4:308. doi:10.3389/fonc.2014.00308.