PuSH - Publication Server of Helmholtz Zentrum München

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1.
Do, K.T. ; Rasp, D.J.N.P. ; Kastenmüller, G. ; Suhre, K. & Krumsiek, J.: MoDentify: Phenotype-driven module identification in metabolomics networks at different resolutions. Bioinformatics 35, 532-534 (2019)
2.
Liebsch, C.* et al.: The saliva metabolome in association to oral health status. J. Dent. Res., accepted (2019)
3.
Adam, J. et al.: Response to comment on Adam et al. Metformin effect on nontargeted metabolite profiles in patients with type 2 diabetes and in multiple murine tissues. Diabetes 2016;65:3776-3785. Diabetes 66, e3-e4 (2017)
4.
Do, K.T. et al.: Phenotype-driven identification of modules in a hierarchical map of multifluid metabolic correlations. NPJ Syst. Biol. Appl. 3:28 (2017)
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Halama, A.* et al.: Nesting of colon and ovarian cancer cells in the endothelial niche is associated with alterations in glycan and lipid metabolism. Sci. Rep. 7:39999 (2017)
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Hertel, J.* et al.: Evidence for stress-like alterations in the HPA-Axis in women taking oral contraceptives. Sci. Rep. 7:14111 (2017)
7.
Molnos, S. et al.: Metabolite ratios as potential biomarkers for type 2 diabetes: A DIRECT study. Diabetologia 61, 117-129 (2017)
8.
Molnos, S. et al.: pulver: An R package for parallel ultra-rapid p-value computation for linear regression interaction terms. BMC Bioinformatics 18:429 (2017)
9.
Piontek, U.* et al.: Sex-specific metabolic profiles of androgens and its main binding protein SHBG in a middle aged population without diabetes. Sci. Rep. 7:2235 (2017)
10.
Suhre, K.* et al.: Connecting genetic risk to disease end points through the human blood plasma proteome. Nat. Commun. 8:14357 (2017)
11.
Toledo, J.B.* et al.: Metabolic network failures in Alzheimer's disease-A biochemical road map. Alzheimers Dement. 13, 965-984 (2017)
12.
van Waateringe, R.P.* et al.: The association between various smoking behaviors, cotinine biomarkers and skin autofluorescence, a marker for advanced glycation end product accumulation. PLoS ONE 12:e0179330 (2017)
13.
Ward-Caviness, C.K. et al.: Improvement of myocardial infarction risk prediction via inflammation-associated metabolite biomarkers. Heart 103, 1278-1285 (2017)
14.
Adam, J. et al.: Metformin effect on non-targeted metabolite profiles in patients with type 2 diabetes and multiple murine tissues. Diabetes 65, 3776-3785 (2016)
15.
Al Muftah, W.A.* et al.: Epigenetic associations of type 2 diabetes and BMI in an Arab population. Clin. Epigenet. 8:13 (2016)
16.
Altmaier, E. et al.: The pharmacogenetic footprint of ACE inhibition: A population-based metabolomics study. PLoS ONE 11:e0153163 (2016)
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Beger, R.D.* et al.: Metabolomics enables precision medicine: “A White Paper, Community Perspective”. Metabolomics 12, 149 (2016)
18.
Cornelis, M.C.* et al.: Genome-wide association study of caffeine metabolites provides new insights to caffeine metabolism and dietary caffeine-consumption behavior. Hum. Mol. Genet. 25, 5472-5482 (2016)
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Halama, A.* et al.: Measurement of 1,5-anhydroglucitol in blood and saliva: From non-targeted metabolomics to biochemical assay. J. Transl. Med. 14:140 (2016)
20.
Knacke, H.* et al.: Metabolic fingerprints of circulating IGF-I and the IGF-I/IGFBP-3 ration: A multi-fluid metabolomics study. J. Clin. Endocrinol. Metab. 101, 4730-4742:jc20162588 (2016)