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Lude Franke

Showing results (21-30 of 265) with videos related to

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Trends in Endocrinology and Metabolism: TEM|November 28, 2006
A strategy to search for common obesity and type 2 diabetes genesClara C Elbers, N Charlotte Onland-Moret, Lude Franke, et al.
Plos One|December 2, 2016
Inter-Tissue Gene Co-Expression Networks between Metabolically Healthy and Unhealthy Obese IndividualsLisette J A Kogelman, Jingyuan Fu, Lude Franke, et al.
Nature Genetics|April 4, 2018
Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLsMonique G P van der Wijst, Harm Brugge, Dylan H de Vries, et al.
Genome Medicine|October 21, 2015
An integrative systems genetics approach reveals potential causal genes and pathways related to obesityLisette J A Kogelman, Daria V Zhernakova, Harm-Jan Westra, et al.
Human Molecular Genetics|January 13, 2016
Comprehensive analysis of schizophrenia-associated loci highlights ion channel pathways and biologically plausible candidate causal genesTune H Pers, Pascal Timshel, Stephan Ripke, et al.
American Journal of Physiology. Gastrointestinal and Liver Physiology|May 14, 2005
No genetic association of the human prolyl endopeptidase gene in the Dutch celiac disease populationBegoña Diosdado, Dariusz T Stepniak, Alienke J Monsuur, et al.
Clinical Gastroenterology and Hepatology : the Official Clinical Practice Journal of the American Gastroenterological Association|March 6, 2007
Neutrophil recruitment and barrier impairment in celiac disease: a genomic studyBegoña Diosdado, Harm van Bakel, Eric Strengman, et al.
Frontiers in Immunology|March 3, 2023
Integration of <i>Candida albicans</i>-induced single-cell gene expression data and secretory protein concentrations reveal genetic regulators of inflammationCollins K Boahen, Roy Oelen, Kieu Le, et al.
BMC Genomics|March 16, 2021
Correction for both common and rare cell types in blood is important to identify genes that correlate with ageDamiano Pellegrino-Coppola, Annique Claringbould, Maartje Stutvoet, et al.
Bioinformatics (Oxford, England)|June 10, 2011
MixupMapper: correcting sample mix-ups in genome-wide datasets increases power to detect small genetic effectsHarm-Jan Westra, Ritsert C Jansen, Rudolf S N Fehrmann, et al.
Pageof 27

Showing results (21-30 of 265) with videos related to

Sort By:
Pageof 27
Trends in Endocrinology and Metabolism: TEM|November 28, 2006
A strategy to search for common obesity and type 2 diabetes genesClara C Elbers, N Charlotte Onland-Moret, Lude Franke, et al.
Plos One|December 2, 2016
Inter-Tissue Gene Co-Expression Networks between Metabolically Healthy and Unhealthy Obese IndividualsLisette J A Kogelman, Jingyuan Fu, Lude Franke, et al.
Nature Genetics|April 4, 2018
Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLsMonique G P van der Wijst, Harm Brugge, Dylan H de Vries, et al.
Genome Medicine|October 21, 2015
An integrative systems genetics approach reveals potential causal genes and pathways related to obesityLisette J A Kogelman, Daria V Zhernakova, Harm-Jan Westra, et al.
Human Molecular Genetics|January 13, 2016
Comprehensive analysis of schizophrenia-associated loci highlights ion channel pathways and biologically plausible candidate causal genesTune H Pers, Pascal Timshel, Stephan Ripke, et al.
American Journal of Physiology. Gastrointestinal and Liver Physiology|May 14, 2005
No genetic association of the human prolyl endopeptidase gene in the Dutch celiac disease populationBegoña Diosdado, Dariusz T Stepniak, Alienke J Monsuur, et al.
Clinical Gastroenterology and Hepatology : the Official Clinical Practice Journal of the American Gastroenterological Association|March 6, 2007
Neutrophil recruitment and barrier impairment in celiac disease: a genomic studyBegoña Diosdado, Harm van Bakel, Eric Strengman, et al.
Frontiers in Immunology|March 3, 2023
Integration of <i>Candida albicans</i>-induced single-cell gene expression data and secretory protein concentrations reveal genetic regulators of inflammationCollins K Boahen, Roy Oelen, Kieu Le, et al.
BMC Genomics|March 16, 2021
Correction for both common and rare cell types in blood is important to identify genes that correlate with ageDamiano Pellegrino-Coppola, Annique Claringbould, Maartje Stutvoet, et al.
Bioinformatics (Oxford, England)|June 10, 2011
MixupMapper: correcting sample mix-ups in genome-wide datasets increases power to detect small genetic effectsHarm-Jan Westra, Ritsert C Jansen, Rudolf S N Fehrmann, et al.
Pageof 27