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Tracey L, Villuendas R, Ortiz P, et al. Identification of genes involved in resistance to interferon-alpha in cutaneous T-cell lymphoma. Am J Pathol. 2002;161:1825-37. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12414529.
Tracey L, Villuendas R, Ortiz P, et al. Identification of genes involved in resistance to interferon-alpha in cutaneous T-cell lymphoma. Am J Pathol. 2002;161:1825-37. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12414529.
Jaime MDLA, Lopez-Llorca LVicente, Conesa A, et al. Identification of yeast genes that confer resistance to chitosan oligosaccharide (COS) using chemogenomics. BMC genomics. 2012;13:267. doi:10.1186/1471-2164-13-267.
Palomero L, Galván-Femenía I, de Cid R, et al. Immune Cell Associations with Cancer Risk. iScience. 2020;23(7):101296. doi:10.1016/j.isci.2020.101296.
Palomero L, Galván-Femenía I, de Cid R, et al. Immune Cell Associations with Cancer Risk. iScience. 2020;23(7):101296. doi:10.1016/j.isci.2020.101296.
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, 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, 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, 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.
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, 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, 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.
Wilkinson MD, Senger M, Kawas E, et al. Interoperability with Moby 1.0--it's better than sharing your toothbrush!. Brief Bioinform. 2008;9(3):220-31. doi:10.1093/bib/bbn003.
Wilkinson MD, Senger M, Kawas E, et al. Interoperability with Moby 1.0--it's better than sharing your toothbrush!. Brief Bioinform. 2008;9(3):220-31. doi:10.1093/bib/bbn003.
Wilkinson MD, Senger M, Kawas E, et al. Interoperability with Moby 1.0–it’s better than sharing your toothbrush!. Brief Bioinform. 2008;9:220-31. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18238804.
Wilkinson MD, Senger M, Kawas E, et al. Interoperability with Moby 1.0–it’s better than sharing your toothbrush!. Brief Bioinform. 2008;9:220-31. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18238804.
L
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.
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.
M
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.
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.
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.
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.