PuSH - Publication Server of Helmholtz Zentrum München

17 Records found.
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1.
Hamad, S. et al.: HitPickV2: A web server to predict targets of chemical compounds. Bioinformatics 35, 1239-1240 (2019)
2.
Arnold, M. ; Raffler, J. ; Suhre, K.* & Kastenmüller, G.: Datenbasierte Funktionsvorhersage krankheitsrelevanter genetischer Varianten. BioSpektrum 24, 662-663 (2018)
3.
Do, K.T. et al.: Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies. Metabolomics 14:128 (2018)
4.
Köttgen, A.* ; Raffler, J. ; Sekula, P.* & Kastenmüller, G.: Genome-wide association studies of metabolite concentrations (mGWAS): Relevance for nephrology. Semin. Nephrol. 38, 151-174 (2018)
5.
Kim, H.I.* et al.: Fine mapping and functional analysis reveal a role of SLC22A1 in acylcarnitine transport. Am. J. Hum. Genet. 101, 489-502 (2017)
6.
Rueedi, R.* et al.: Metabomatching: Using genetic association to identify metabolites in proton NMR spectroscopy. PLoS Comput. Biol. 13, e1005839:e1005839 (2017)
7.
Suhre, K.* et al.: Connecting genetic risk to disease end points through the human blood plasma proteome. Nat. Commun. 8:14357 (2017)
8.
Suhre, K. ; Raffler, J. & Kastenmüller, G.: Biochemical insights from population studies with genetics and metabolomics. Arch. Biochem. Biophys. 589, 168-176 (2016)
9.
Arnold, M. ; Raffler, J. ; Pfeufer, A. ; Suhre, K. & Kastenmüller, G.: SNiPA: An interactive, genetic variant-centered annotation browser. Bioinformatics 31, 1334-1336 (2015)
10.
Kastenmüller, G. ; Raffler, J. ; Gieger, C. & Suhre, K.: Genetics of human metabolism: An update. Hum. Mol. Genet. 24, R93-R101 (2015)
11.
Raffler, J. et al.: Genome-wide association study with targeted and non-targeted NMR metabolomics identifies 15 novel loci of urinary human metabolic individuality. PLoS Genet. 11:e1005487 (2015)
12.
Raffler, J.: Genomweite Assoziationsstudien mit metabolischen Merkmalen aus Kernspinresonanz (NMR) Spektroskopie. München, Ludwig-Maximilians-Universität, Fakultät für Biologie, Diss., 2015, 187 S.
13.
Raffler, J. et al.: Identification and MS-assisted interpretation of genetically influenced NMR signals in human plasma. Genome Med. 5:13 (2013)
14.
Krug, S.* et al.: The dynamic range of the human metabolome revealed by challenges. FASEB J. 26, 2607-2619 (2012)
15.
Petersen, A.-K. et al.: Genetic associations with lipoprotein subfractions provide information on their biological nature. Hum. Mol. Genet. 21, 1433-1443 (2012)
16.
Suhre, K. et al.: A genome-wide association study of metabolic traits in human urine. Nat. Genet. 43, 565-571 (2011)
17.
Suhre, K. et al.: Human metabolic individuality in biomedical and pharmaceutical research. Nature 477, 54-60 (2011)