PuSH - Publication Server of Helmholtz Zentrum München

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21.
Hilsenbeck, O. et al.: Software tools for single-cell tracking and quantification of cellular and molecular properties. Nat. Biotechnol. 34, 703-706 (2016)
22.
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)
23.
Marr, C. ; Zhou, J.X.* & Huang, S.*: Single-cell gene expression profiling and cell state dynamics: Collecting data, correlating data points and connecting the dots. Curr. Opin. Biotechnol. 39, 207-214 (2016)
24.
Mishra, M. et al.: Structure-based assessment of cancerous mitochondria using deep networks. In: Proceedings (Proceedings / IEEE 13th International Symposium on Biomedical Imaging (ISBI), 13-16 April 2016, Prague, Czech Republic). IEEE, 2016.
25.
Feigelman, J. ; Popovic, N.* & Marr, C.: A case study on the use of scale separation-based analytical propagators for parameter inference in models of stochastic gene regulation. J. Coupled Syst. Multiscale Dyn. 3, 164-173 (2015)
26.
Filipczyk, A. et al.: Network plasticity of pluripotency transcription factors in embryonic stem cells. Nat. Cell Biol. 17, 1235-1246 (2015)
27.
Hug, S. ; Schwarzfischer, M. ; Hasenauer, J. ; Marr, C. & Theis, F.J.: An adaptive scheduling scheme for calculating Bayes factors with thermodynamic integration using Simpson’s rule. Stat. Comp. 26, 663-677 (2015)
28.
Loos, C. ; Marr, C. ; Theis, F.J. & Hasenauer, J.: Approximate bayesian computation for stochastic single-cell time-lapse data using multivariate test statistics. Lecture Notes Comp. Sci. 9308, 52-63 (2015)
29.
Marr, C. ; Hasenauer, J. & Theis, F.J.: Models and methods for systems biology and systems medicine: The Institute of Computational Biology at the Helmholtz Zentrum München. Systembiologie.de 9, 40-43 (2015)
30.
Popovic, N.* ; Marr, C. & Swain, P.S.*: A geometric analysis of fast-slow models for stochastic gene expression. J. Math. Biol. 72, 87-122 (2015)
31.
Preusse, M. et al.: SimiRa: A tool to identify coregulation between microRNAs and RNA-binding proteins. RNA Biol. 12, 998-1009 (2015)
32.
Strasser, M. ; Feigelman, J. ; Theis, F.J. & Marr, C.: Inference of spatiotemporal effects on cellular state transitions from time-lapse microscopy. BMC Syst. Biol. 9:61 (2015)
33.
Theis, F.J. ; Ugelvig, L.V.* ; Marr, C. & Cremer, S.*: Opposing effects of allogrooming on disease transmission in ant societies. Philos. Trans. R. Soc. B - Biol. Sci. 370, DOI: 10.1098/rstb.2014.0108 (2015)
34.
Wang, L. et al.: Anatomic-landmark detection using graphical context modelling. In: Proceedings (12th IEEE International Symposium on Biomedical Imaging, ISBI 2015, 16-19 April 2015, Brooklyn, United States). 2015. 1304-1307
35.
Feigelman, J. ; Theis, F.J. & Marr, C.: MCA: Multiresolution Correlation Analysis, a graphical tool for subpopulation identification in single-cell gene expression data. BMC Bioinformatics 15:240 (2014)
36.
Khakhutskyy, V.* et al.: Centroid clustering of cellular lineage trees. Lecture Notes Comp. Sci. 8649, 15-29 (2014)
37.
Reversat, A.* et al.: Morphodynamic analysis of T lymphocyte migration in confined microenvironments. Mol. Biol. Cell 25:P537 (2014)
38.
Buggenthin, F. et al.: An automatic method for robust and fast cell detection in bright field images from high-throughput microscopy. BMC Bioinformatics 14:297 (2013)
39.
Konrad, M.* et al.: Social transfer of pathogenic fungus promotes active immunisation in ant colonies. PLoS Biol. 10:e1001300 (2012)
40.
Marr, C. & Hütt, M.-T.*: Cellular automata on graphs: Topological properties of ER graphs evolved towards low-entropy dynamics. Entropy 14, 993-1010 (2012)