PuSH - Publication Server of Helmholtz Zentrum München

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81.
Dharuri, H.* et al.: Automated wokflow-based exploitation of pathway databases provides new insights into genetic associations of metabolite profiles. BMC Genomics 14:865 (2013)
82.
Goek, O.-N.* et al.: Metabolites associate with kidney function decline and incident chronic kidney disease in the general population. Nephrol. Dial. Transplant. 28, 2131-2138 (2013)
83.
Jaremek, M. et al.: Alcohol-induced metabolomic differences in humans. Transl. Psychiatry 3:e276 (2013)
84.
Menni, C.* et al.: Targeted metabolomics profiles are strongly correlated with nutritional patterns in women. Catal. Lett. 9, 506-514 (2013)
85.
Menni, C.* et al.: Metabolomic markers reveal novel pathways of ageing and early development in human populations. Int. J. Epidemiol. 42, 1111-1119 (2013)
86.
Menni, C.* et al.: Biomarkers for type 2 diabetes and impaired fasting glucose using a non-targeted metabolomics approach. Diabetes 62, 4270-4276 (2013)
87.
Mook-Kanamori, M.J.* et al.: Ethnic and gender differences in advanced glycation end products measured by skin auto-fluorescence. Dermatoendocrinol. 5, 325-330 (2013)
88.
Raffler, J. et al.: Identification and MS-assisted interpretation of genetically influenced NMR signals in human plasma. Genome Med. 5:13 (2013)
89.
Ried, J.S. et al.: Integrative genetic and metabolite profiling analysis suggests altered phosphatidylcholine metabolism in asthma. Allergy 68, 629-636 (2013)
90.
Widmann, P.* et al.: A systems biology approach using metabolomic data reveals genes and pathways interacting to modulate divergent growth in cattle. BMC Genomics 14:798 (2013)
91.
Xu, T. et al.: Effects of smoking and smoking cessation on human serum metabolite profile: Results from the KORA cohort study. BMC Med. 11:60 (2013)
92.
Artati, A. ; Prehn, C. ; Möller, G. & Adamski, J.: Assay tools for metabolomics. In: Suhre, K.* [Eds.]: Genetics Meets Metabolomics: From Experiment to Systems Biology. New York: Springer, 2012. 13-38
93.
Föcker, M.* et al.: Comparison of metabolic profiles of acutely ill and short-term weight recovered patients with anorexia nervosa reveals alterations of 33 out of 163 metabolites. J. Psychiatr. Res. 46, 1600-1609 (2012)
94.
Fuchs, H. et al.: Mouse genetics and metabolic mouse phenotyping. In: Suhre, K.* [Eds.]: Genetics Meets Metabolomics: from Experiment to Systems Biology. Springer, 2012. 85-106
95.
Gieger, C.: Statistical methods in genetic and molecular epidemiology and their application in studies with metabolic phenotypes. In: Suhre, K.* [Eds.]: Genetics Meets Metabolomics: From Experiment to Systems Biology. New York: Springer, 2012. 39-56
96.
Goek, O.N.* et al.: Serum metabolite concentrations and decreased GFR in the general population. Am. J. Kidney Dis. 60, 197-206 (2012)
97.
Illig, T.: Pre-conditions for high quality biobanking in large human epidemiological cohorts for metabolomics and other - Omics studies. In: Suhre, K.* [Eds.]: Genetics Meets Metabolomics: From Experiment to Systems Biology. New York: Springer, 2012. 5-11
98.
Jourdan, C. et al.: Body fat free mass is associated with the serum metabolite profile in a population-based study. PLoS ONE 7:e40009 (2012)
99.
Krug, S.* et al.: The dynamic range of the human metabolome revealed by challenges. FASEB J. 26, 2607-2619 (2012)
100.
Krumsiek, J. ; Suhre, K. ; Illig, T. ; Adamski, J. & Theis, F.J.: Bayesian independent component analysis recovers pathway signatures from blood metabolomics data. J. Proteome Res. 11, 4120-4131 (2012)