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Pieper U, Eswar N, Braberg H, et al. MODBASE, a database of annotated comparative protein structure models, and associated resources. Nucleic Acids Res. 2004;32:D217-22. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14681398.
Corrales P, Martin-Taboada M, Vivas-García Y, et al. microRNAs-mediated regulation of insulin signaling in white adipose tissue during aging: Role of caloric restriction. Aging Cell. 2023:e13919. doi:10.1111/acel.13919.
Conesa A, Forment J, Gadea J, van Dijk J. Microarray Technology in Agricultural Research. In: Microarray Technology Through Applications. Microarray Technology Through Applications. F. Falciani. Publisher: Taylor and Francis Group; 2007:173-209.
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.
The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nature Biotechnology. 2010;28(8):827 - 838. doi:10.1038/nbt.1665.
Dopazo J. Microarray Data Processing And Analysis. In: Microarray data analysis II. Microarray data analysis II. Kluwer Academic; 2002:43-63.
Rizza S, Conesa A, Juarez J, et al. Microarray analysis of Etrog citron (Citrus medica L.) reveals changes in chloroplast, cell wall, peroxidase and symporter activities in response to viroid infection. Molecular plant pathology. 2012. doi:10.1111/j.1364-3703.2012.00794.x.
Mateos A, Herrero J, Tamames J, Dopazo J. Methods of Microarray Data Analysis IISupervised Neural Networks for Clustering Conditions in DNA Array Data After Reducing Noise by Clustering Gene Expression Profiles. (Lin SM, Johnson KF, eds.). Boston: Kluwer Academic Publishers; 2002:91 - 103. doi:10.1007/b11298210.1007/0-306-47598-7_7.
Dopazo J, Zanders E, Dragoni I, Amphlett G, Falciani F. Methods and approaches in the analysis of gene expression data. J Immunol Methods. 2001;250:93-112. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11251224.
Sola-García A, Cáliz-Molina MÁngeles, Espadas I, et al. Metabolic reprogramming by Acly inhibition using SB-204990 alters glucoregulation and modulates molecular mechanisms associated with aging. Commun Biol. 2023;6(1):250. doi:10.1038/s42003-023-04625-4.
Brumos J, Colmenero-Flores JM, Conesa A, et al. Membrane transporters and carbon metabolism implicated in chloride homeostasis differentiate salt stress responses in tolerant and sensitive Citrus rootstocks. Funct Integr Genomics. 2009. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=19190944.
Cubuk C, Can FE, Peña-Chilet M, Dopazo J. Mechanistic Models of Signaling Pathways Reveal the Drug Action Mechanisms behind Gender-Specific Gene Expression for Cancer Treatments. Cells. 2020;9(7). doi:10.3390/cells9071579.
Falco MM, Peña-Chilet M, Loucera C, Hidalgo MR, Dopazo J. Mechanistic models of signaling pathways deconvolute the glioblastoma single-cell functional landscapeAbstract. NAR Cancer. 2020;2(2). doi:10.1093/narcan/zcaa011.
Rian K, Esteban-Medina M, Hidalgo MR, et al. Mechanistic modeling of the SARS-CoV-2 disease map. BioData Min. 2021;14(1):5. doi:10.1186/s13040-021-00234-1.
Esteban-Medina M, Loucera C, Rian K, et al. The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery. J Transl Med. 2024;22(1):139. doi:10.1186/s12967-024-04911-7.
Sánchez-Tena S, Reyes-Zurita FJ, Díaz-Moralli S, et al. Maslinic Acid-Enriched Diet Decreases Intestinal Tumorigenesis in Apc(Min/+) Mice through Transcriptomic and Metabolomic Reprogramming. PloS one. 2013;8:e59392. doi:10.1371/journal.pone.0059392.
Conesa A, Nueda MJ, Ferrer A, Talon M. maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics. 2006;22:1096-102. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16481333.
Mapping the human genetic architecture of COVID-19. Nature. 2021;600(7889):472-477. doi:10.1038/s41586-021-03767-x.
Carbonell J, Alloza E, Arce P, et al. A map of human microRNA variation uncovers unexpectedly high levels of variability. Genome medicine. 2012;4:62. doi:10.1186/gm363.
Iglesias JManuel, Beloqui I, Garcia-Garcia F, et al. Mammosphere Formation in Breast Carcinoma Cell Lines Depends upon Expression of E-cadherin. PLoS ONE. 2013;8:e77281 -. Available at: http://dx.doi.org/10.1371%2Fjournal.pone.0077281.
Iglesias JManuel, Beloqui I, Garcia-Garcia F, et al. Mammosphere formation in breast carcinoma cell lines depends upon expression of E-cadherin. PLoS One. 2013;8(10):e77281. doi:10.1371/journal.pone.0077281.
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Cobo-Vuilleumier N, Lorenzo PI, Rodríguez NGarcía, et al. LRH-1 agonism favours an immune-islet dialogue which protects against diabetes mellitus. Nat Commun. 2018;9(1):1488. doi:10.1038/s41467-018-03943-0.
Rossi A, Marti-Renom MA, Sali A. Localization of binding sites in protein structures by optimization of a composite scoring function. Protein Sci. 2006;15:2366-80. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16963645.
Gabaldón T, Huynen MA. Lineage-specific gene loss following mitochondrial endosymbiosis and its potential for function prediction in eukaryotes. Bioinformatics. 2005;21 Suppl 2:ii144-50. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16204094.
Yung S, Ledran M, Moreno-Gimeno I, et al. Large-scale transcriptional profiling and functional assays reveal important roles for Rho-GTPase signalling and SCL during haematopoietic differentiation of human embryonic stem cells. Hum Mol Genet. 2011;20(24):4932-46. doi:10.1093/hmg/ddr431.