PuSH - Publication Server of Helmholtz Zentrum München

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101.
Feigelman, J. et al.: Analysis of cell lineage trees by exact bayesian inference identifies negative autoregulation of nanog in mouse embryonic stem cells. Cell Syst. 3, 480-490 (2016)
102.
Fiedler, A. ; Raeth, S.* ; Theis, F.J. ; Hausser, A.* & Hasenauer, J.: Tailored parameter optimization methods for ordinary differential equation models with steady-state constraints. BMC Syst. Biol. 10:80 (2016)
103.
Förster, K. et al.: LSC Abstract – Early biomarkers indicating the development of neonatal chronic lung disease defined by clinical and imaging parameters. Eur. Respir. J. 48, PP104 (2016)
104.
Fröhlich, F. et al.: Inference for stochastic chemical kinetics using moment equations and system size expansion. PLoS Comput. Biol. 12:e1005030 (2016)
105.
Fröhlich, F. et al.: Large-scale modeling of cancer signaling: Mechanistic modeling meets Big Data. Eur. J. Cancer 68, S44-S44 (2016)
106.
Garzorz-Stark, N.* et al.: A novel molecular disease classifier for psoriasis and eczema. Exp. Dermatol. 25, 767-774 (2016)
107.
Geissen, E.M.* et al.: MEMO - multi-experiment mixture model analysis of censored data. Bioinformatics 32, 2464-2472 (2016)
108.
González-Valllinas, M.* et al.: Abstract 1945: Identification of a miRNA/mRNA network driving non-small cell lung cancer (NSCLC) dissemination. Cancer Res. 76 (2016)
109.
Haffner, I.* et al.: Clinical validation of response and resistance factor candidates to targeted therapy in Gastric Cancer (GC). Oncol. Res. Treat. 39, 16-17 (2016)
110.
Haghverdi, L. ; Büttner, M. ; Wolf, F.A. ; Buettner, F. & Theis, F.J.: Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845-848 (2016)
111.
Hilsenbeck, O. et al.: Software tools for single-cell tracking and quantification of cellular and molecular properties. Nat. Biotechnol. 34, 703-706 (2016)
112.
Hoppe, P.S. et al.: Early myeloid lineage choice is not initiated by random PU.1 to GATA1 protein ratios. Nature 535, 299-302 (2016)
113.
Hross, S. ; Fiedler, A. ; Theis, F.J. & Hasenauer, J.: Quantitative comparison of competing PDE models for Pomlp dynamics in fission yeast. In: (Foundations of Systems Biology in Engineering - FOSBE 2016, 9—12 October 2016, Magdeburg). 2016. 264-269 (IFAC PapersOnline ; 49-26)
114.
Hug, S. ; Schmidl, D. ; Li, W.B. ; Greiter, M. & Theis, F.J.: Bayesian model selection methods and their application to biological ODE systems. In: Geris, L.* ; Gomez-Cabrero, D.* [Eds.]: Uncertainty in Biology : A Computational Modeling Approach. Berlin; Heidelberg: Springer, 2016. 243-268 (Stud. Mechanobiol. Tiss. Engineering Biomater. ; 17)
115.
Huypens, P. et al.: Epigenetic germline inheritance of diet-induced obesity and insulin resistance. Nat. Genet. 48, 497-499 (2016)
116.
Huypens, P. et al.: Epigenetic germline inheritance of diet-induced obesity and insulin resistance. Obstet. Gynecol. Surv. 71, 719-720 (2016)
117.
Jung, B. et al.: Pitchfork and Gprasp2 target smoothened to the primary cilium for Hedgehog pathway activation. PLoS ONE 11:e0149477 (2016)
118.
Kazeroonian, A. ; Fröhlich, F. ; Raue, A.* ; Theis, F.J. & Hasenauer, J.: CERENA: ChEmical REaction Network Analyzer - a toolbox for the simulation and analysis of stochastic chemical kinetics. PLoS ONE 11:e0146732 (2016)
119.
Kondofersky, I. ; Theis, F.J. & Fuchs, C.: Inferring catalysis in biological systems. IET Syst. Biol. 10, 210-218 (2016)
120.
Kong, B.* et al.: A subset of metastatic pancreatic ductal adenocarcinomas depends quantitatively on oncogenic Kras/Mek/Erk-induced hyperactive mTOR signalling. Gut 65, 647-657 (2016)