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1.
Chak, C.M. et al.: Ageing investigation using two-time-point metabolomics data from KORA and CARLA studies. Metabolites 9:44 (2019)
2.
Gerlini, R. et al.: Glucose tolerance and insulin sensitivity define adipocyte transcriptional programs in human obesity. Mol. Metab. 18, 42-50 (2018)
3.
Rotter, M. et al.: Night shift work affects urine metabolite profiles of nurses with early chronotype. Metabolites 8:45 (2018)
4.
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)
5.
Rotter, M. et al.: Stability of targeted metabolite profiles of urine samples under different storage conditions. Metabolomics 13:4 (2017)
6.
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)
7.
Fall, T.* et al.: Non-targeted metabolomics combined with genetic analyses identifies bile acid synthesis and phospholipid metabolism as being associated with incident type 2 diabetes. Diabetologia 59, 2114-2124 (2016)
8.
Nowak, C.* et al.: Effect of insulin resistance on monounsaturated fatty acid levels: A multi-cohort non-targeted metabolomics and mendelian randomization study. PLoS Genet. 12:e1006379 (2016)
9.
Pirovano, A.* et al.: QSARs for estimating intrinsic hepatic clearance of organic chemicals in humans. Environ. Toxicol. Pharmacol. 42, 190-197 (2016)
10.
Abdelaziz, A.* et al.: Using online tool (iPrior) for modeling ToxCast™ assays towards prioritization of animal toxicity testing. Comb. Chem. High Throughput Screen. 18, 420-438 (2015)
11.
Brandmaier, S. et al.: Response to Comment on Xu et al. Effects of metformin on metabolite profiles and LDL cholesterol in patients with type 2 diabetes. Diabetes Care 2015;38:1858-1867. Diabetes Care 38, e216-e217 (2015)
12.
Ly-Verdú, S. et al.: Combining metabolomic non-targeted GC×GC-ToF-MS analysis and chemometric ASCA-based study of variances to assess dietary influence on type 2 diabetes development in a mouse model. Anal. Bioanal. Chem. 407, 343-354 (2015)
13.
Pirovano, A.* et al.: The utilisation of structural descriptors to predict metabolic constants of xenobiotics in mammals. Environ. Toxicol. Pharmacol. 39, 247-258 (2015)
14.
Xu, T. et al.: Effects of metformin on metabolite profiles and LDL cholesterol in patients with type 2 diabetes. Diabetes Care 38, 1858-1867 (2015)
15.
Brandmaier, S. et al.: The QSPR-THESAURUS: The online platform of the CADASTER project. ATLA-Altern. Lab. Anim. 42, 13-24 (2014)
16.
Tetko, I.V. et al.: Experimental and theoretical studies in the EU FP7 Marie Curie Initial Training Network Project, Environmental ChemOinformatics (ECO). ATLA-Altern. Lab. Anim. 42, 7-11 (2014)
17.
Brandmaier, S. ; Novotarskyi, S.* ; Sushko, I.* & Tetko, I.V.: From descriptors to predicted properties: Experimental design by using applicability domain estimation. ATLA-Altern. Lab. Anim. 41, 33-47 (2013)
18.
Brandmaier, S. & Tetko, I.V.: Robustness in experimental design: A study on the reliability of selection approaches. Comp. Struc. Biotech. J. 7:e201305002 (2013)
19.
Cassani, S.* et al.: Evaluation of CADASTER QSAR models for the aquatic toxicity of (benzo)triazoles and prioritisation by consensus prediction. ATLA-Altern. Lab. Anim. 41, 49-64 (2013)
20.
Tetko, I.V. et al.: Prioritisation of Polybrominated Diphenyl Ethers (PBDEs) by using the QSPR-THESAURUS web tool. ATLA-Altern. Lab. Anim. 41, 127-135 (2013)