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Integrated gene set analysis for microRNA studies. Bioinformatics. 2016;32(18):2809-16. doi:10.1093/bioinformatics/btw334.
. 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.
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.
The Mutational Landscape of Acute Promyelocytic Leukemia Reveals an Interacting Network of Co-Occurrences and Recurrent Mutations. PLoS One. 2016;11(2):e0148346. doi:10.1371/journal.pone.0148346.
The Mutational Landscape of Acute Promyelocytic Leukemia Reveals an Interacting Network of Co-Occurrences and Recurrent Mutations. PLoS One. 2016;11(2):e0148346. doi:10.1371/journal.pone.0148346.
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.
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.
Serum metabolomic profiling facilitates the non-invasive identification of metabolic biomarkers associated with the onset and progression of non-small cell lung cancer. Oncotarget. 2016;7(11):12904-16. doi:10.18632/oncotarget.7354.
Stress-induced activation of brown adipose tissue prevents obesity in conditions of low adaptive thermogenesis. Mol Metab. 2016;5(1):19-33. doi:10.1016/j.molmet.2015.10.005.
The transcriptomics of an experimentally evolved plant-virus interaction. Sci Rep. 2016;6:24901. doi:10.1038/srep24901.
. Web-based network analysis and visualization using CellMaps. Bioinformatics. 2016;32(19):3041-3. doi:10.1093/bioinformatics/btw332.
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.
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.
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.
GGPS1 Mutation and Atypical Femoral Fractures with Bisphosphonates. N Engl J Med. 2017;376(18):1794-1795. doi:10.1056/NEJMc1612804.
Roca-Ayats-2017NEJM - GGPS1 Mutation and Atypical Femoral Fractures with Bisphosphonates.pdf (214.03 KB)
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GGPS1 Mutation and Atypical Femoral Fractures with Bisphosphonates. N Engl J Med. 2017;376(18):1794-1795. doi:10.1056/NEJMc1612804.
Roca-Ayats-2017NEJM - GGPS1 Mutation and Atypical Femoral Fractures with Bisphosphonates.pdf (214.03 KB)
![application/pdf PDF icon](/modules/file/icons/application-pdf.png)
Global Transcriptome Analysis of Primary Cerebrocortical Cells: Identification of Genes Regulated by Triiodothyronine in Specific Cell Types. Cereb Cortex. 2017;27(1):706-717. doi:10.1093/cercor/bhv273.
. Global Transcriptome Analysis of Primary Cerebrocortical Cells: Identification of Genes Regulated by Triiodothyronine in Specific Cell Types. Cereb Cortex. 2017;27(1):706-717. doi:10.1093/cercor/bhv273.
. HGVA: the Human Genome Variation Archive. Nucleic Acids Res. 2017;45(W1):W189-W194. doi:10.1093/nar/gkx445.
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.
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.
Mutations in TRAPPC11 are associated with a congenital disorder of glycosylation. Hum Mutat. 2017;38(2):148-151. doi:10.1002/humu.23145.
Mutations in TRAPPC11 are associated with a congenital disorder of glycosylation. Hum Mutat. 2017;38(2):148-151. doi:10.1002/humu.23145.
Mutations in TRAPPC11 are associated with a congenital disorder of glycosylation. Hum Mutat. 2017;38(2):148-151. doi:10.1002/humu.23145.