PuSH - Publikationsserver des Helmholtz Zentrums München

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1.
Jiang, D. et al.: Visualizing scar development in living tissues reveals the collective migration of fibroblasts mediated by N-cadherin. Exp. Dermatol. 28, E13-E13 (2019)
2.
Tischler, J.* et al.: Metabolic regulation of pluripotency and germ cell fate through alpha-ketoglutarate. EMBO J. 38:e99518 (2019)
3.
Backofen, R.* et al.: MicroRNA as an integral part of cell communication: Regularized target prediction and network prediction. In: Lecture Notes in Bioengineerin. 2018. 85-100
4.
Bast, L. et al.: Increasing neural stem cell division asymmetry and quiescence are predicted to contribute to the age-related decline in neurogenesis. Cell Rep. 25, 3231-3240.e8 (2018)
5.
Chatzopoulou, E.I.* et al.: A single-cell micro-trench platform for automatic monitoring of cell division and apoptosis after chemotherapeutic drug administration. Sci. Rep. 8:18042 (2018)
6.
Feigelman, J* ; Weindl, D. ; Theis, F.J. ; Marr, C. & Hasenauer, J.: LNA++: Linear noise approximation with first and second order sensitivities. In: Lecture Notes in Computer Science (16th International Conference on Computational Methods in Systems Biology, 12-14 September 2018, Brno; Czech Republic). 2018. 300-306 ( ; 11095 LNBI)
7.
Kyncl, M.* et al.: Data driven computational modeling of hematopoiesis in myelodysplastic syndromes unveils differences in hematopoietic stem cell kinetics compared to age-matched healthy controls. Blood 132 (2018)
8.
Matek, C. ; Marr, C. & Spiekermann, K.*: Digital cytomorphology: Deep learning on an image data set of cell morphologies in acute myeloid leukemia. In: (Workshop on Bildverarbeitung fur die Medizin, 11-13 March 2018, Erlangen). 2018. 10-10
9.
Strasser, M. et al.: Lineage marker synchrony in hematopoietic genealogies refutes the PU.1/GATA1 toggle switch paradigm. Nat. Commun. 9:2697 (2018)
10.
Viader Llargues, O. ; Lupperger, V. ; Pola-Morell, L. ; Marr, C. & López-Schier, H.: Live cell-lineage tracing and machine learning reveal patterns of organ regeneration. eLife 7:e30823 (2018)
11.
Blasi, T. ; Buettner, F. ; Strasser, M. ; Marr, C. & Theis, F.J.: CgCorrect: A method to correct for confounding cell-cell variation due to cell growth in single-cell transcriptomics. Phys. Biol. 14:036001 (2017)
12.
Buggenthin, F. et al.: Prospective identification of hematopoietic lineage choice by deep learning. Nat. Methods, DOI: 10.1038/nmeth.4182 (2017)
13.
Hilsenbeck, O.* et al.: fastER: A user-friendly tool for ultrafast and robust cell segmentation in large-scale microscopy. Bioinformatics 33, 2020-2028 (2017)
14.
Lupperger, V. ; Buggenthin, F. ; Chapouton, P. & Marr, C.: Image analysis of neural stem cell division patterns in the zebrafish brain. Cytometry A 93A, 314-322 (2017)
15.
Peng, T. et al.: A BaSiC tool for background and shading correction of optical microscopy images. Nat. Commun. 8:14836 (2017)
16.
Veerman, F.* ; Marr, C. & Popović, N.*: Time-dependent propagators for stochastic models of gene expression: An analytical method. J. Math. Biol., 1-52 (2017)
17.
Angerer, P. et al.: Destiny: Diffusion maps for large-scale single-cell data in R. Bioinformatics 32, 1241-1243 (2016)
18.
Blasi, T. et al.: Combinatorial histone acetylation patterns are generated by motif-specific reactions. Cell Syst. 2, 49-58 (2016)
19.
Dragoi, D. et al.: Twist1 induces distinct cell states depending on TGFBR1-activation. Oncotarget 7, 30396-30407 (2016)
20.
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)