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101.
Krumsiek, J. et al.: Mining the unknown: A systems approach to metabolite identification combining genetic and metabolic information. PLoS Genet. 8:e1003005 (2012)
102.
Krumsiek, J. ; Stückler, F. ; Kastenmüller, G. & Theis, F.J.: Systems biology meets metabolism. In: Suhre, K.* [Eds.]: Genetics Meets Metabolomics: from Experiment to Systems Biology. New York: Springer, 2012. 281-313
103.
Petersen, A.-K. et al.: Genetic associations with lipoprotein subfractions provide information on their biological nature. Hum. Mol. Genet. 21, 1433-1443 (2012)
104.
Petersen, A.-K. et al.: On the hypothesis-free testing of metabolite ratios in genome-wide and metabolome-wide association studies. BMC Bioinformatics 13:120 (2012)
105.
Renner, S.* et al.: Changing metabolic signatures of amino acids and lipids during the prediabetic period in a pig model with impaired incretin function and reduced β-cell mass. Diabetes 61, 2166-2175 (2012)
106.
Ried, J.S. et al.: PSEA: Phenotype Set Enrichment Analysis - a new method for analysis of multiple phenotypes. Genet. Epidemiol. 36, 244-252 (2012)
107.
Römisch-Margl, W. et al.: Procedure for tissue sample preparation and metabolite extraction for high-throughput targeted metabolomics. Metabolomics 8, 133-142 (2012)
108.
Suhre, K. & Gieger, C.: Genetic variation in metabolic phenotypes: Study designs and applications. Nat. Rev. Genet. 13, 759-769 (2012)
109.
Wägele, B. ; Witting, M. ; Schmitt-Kopplin, P. & Suhre, K.: MassTRIX reloaded: Combined analysis and visualization of transcriptome and metabolome data. PLoS ONE 7:e39860 (2012)
110.
Wahl, S. et al.: Childhood obesity is associated with changes in the serum metabolite profile. Obes. Facts 5, 660-670 (2012)
111.
Wang-Sattler, R. et al.: Novel biomarkers for pre-diabetes identified by metabolomics. Mol. Syst. Biol. 8:615 (2012)
112.
Witting, M. ; Lucio, M. & Tziotis, D.: Ultrahigh resolution mass spectrometry based non-targeted microbial metabolomics. In: Suhre, K.* [Eds.]: Genetics Meets Metabolomics: From Experiment to Systems Biology. Heidelberg: Springer, 2012. 57-71
113.
Yu, Z. et al.: Human serum metabolic profiles are age dependent. Aging Cell 11, 960-967 (2012)
114.
Altmaier, E. et al.: Questionnaire-based self-reported nutrition habits associate with serum metabolism as revealed by quantitative targeted metabolomics. Eur. J. Epidemiol. 26, 145-156 (2011)
115.
Fohse, L.* et al.: High TCR diversity ensures optimal function and homeostasis of Foxp3+ regulatory T cells. Eur. J. Immunol. 41, 3101-3113 (2011)
116.
Fuchs, H. et al.: Mouse phenotyping. Methods 53, 120-135 (2011)
117.
Kastenmüller, G. ; Römisch-Margl, W. ; Wägele, B. ; Altmaier, E. & Suhre, K.: metaP-Server: A web-based metabolomics data analysis tool. J. Biomed. Biotechnol. 2011:839862 (2011)
118.
Krumsiek, J. ; Suhre, K. ; Illig, T. ; Adamski, J. & Theis, F.J.: Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data. BMC Syst. Biol. 5:21 (2011)
119.
Mittelstraß, K. et al.: Discovery of sexual dimorphisms in metabolic and genetic biomarkers. PLoS Genet. 7:e1002215 (2011)
120.
Nicholson, G.* et al.: A genome-wide metabolic QTL analysis in Europeans implicates two loci shaped by recent positive selection. PLoS Genet. 7:e1002270 (2011)