Export 14 results:
Author Title Type [ Year
Filters: Author is Dopazo, Joaquin and Keyword is Phenotype [Clear All Filters]
Direct functional assessment of the composite phenotype through multivariate projection strategies. Genomics. 2008;92(6):373-83. doi:10.1016/j.ygeno.2008.05.015.
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.
. SNPeffect 4.0: on-line prediction of molecular and structural effects of protein-coding variants. Nucleic Acids Res. 2012;40(Database issue):D935-9. doi:10.1093/nar/gkr996.
Role of CPI-17 in restoring skin homoeostasis in cutaneous field of cancerization: effects of topical application of a film-forming medical device containing photolyase and UV filters. Exp Dermatol. 2013;22(7):494-6. doi:10.1111/exd.12177.
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.
The role of the interactome in the maintenance of deleterious variability in human populations. Mol Syst Biol. 2014;10:752. doi:10.15252/msb.20145222.
Improving the management of Inherited Retinal Dystrophies by targeted sequencing of a population-specific gene panel. Sci Rep. 2016;6:23910. doi:10.1038/srep23910.
Mutations in the MORC2 gene cause axonal Charcot-Marie-Tooth disease. Brain. 2016;139(Pt 1):62-72. doi:10.1093/brain/awv311.
Screening of CD96 and ASXL1 in 11 patients with Opitz C or Bohring-Opitz syndromes. Am J Med Genet A. 2016;170A(1):24-31. doi:10.1002/ajmg.a.37418.
Genomic expression differences between cutaneous cells from red hair color individuals and black hair color individuals based on bioinformatic analysis. Oncotarget. 2017;8(7):11589-11599. doi:10.18632/oncotarget.14140.
Mutations in TRAPPC11 are associated with a congenital disorder of glycosylation. Hum Mutat. 2017;38(2):148-151. doi:10.1002/humu.23145.
Gene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape. Cancer Res. 2018;78(21):6059-6072. doi:10.1158/0008-5472.CAN-17-2705.
Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models. NPJ Syst Biol Appl. 2019;5:7. doi:10.1038/s41540-019-0087-2.
Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models. BMC Bioinformatics. 2019;20(1):370. doi:10.1186/s12859-019-2969-0.
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