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1.
Fiorentino, J. & Scialdone, A.: The role of cell geometry and cell-cell communication in gradient sensing. PLoS Comput. Biol. 18:e1009552 (2022)
2.
Schuh, L. et al.: Altered expression response upon repeated gene repression in single yeast cells. PLoS Comput. Biol. 18:e1010640 (2022)
3.
Sommer, A. et al.: A randomization-based causal inference framework for uncovering environmental exposure effects on human gut microbiota. PLoS Comput. Biol. 18:e1010044 (2022)
4.
Karollus, A.* ; Avsec, Ž.* & Gagneur, J.: Predicting mean ribosome load for 5'UTR of any length using deep learning. PLoS Comput. Biol. 17, e1008982 (2021)
5.
Schmiester, L. et al.: PEtab-Interoperable specification of parameter estimation problems in systems biology. PLoS Comput. Biol. 17:e1008646 (2021)
6.
Dorigatti, E. & Schubert, B.: Graph-theoretical formulation of the generalized epitope-based vaccine design problem. PLoS Comput. Biol. 16:e1008237 (2020)
7.
Hager, P. ; Mewes, H.-W.* ; Rohlfs, M.* ; Klein, C.* & Jeske, T.: SmartPhase: Accurate and fast phasing of heterozygous variant pairs for genetic diagnosis of rare diseases. PLoS Comput. Biol. 16:e1007613 (2020)
8.
Knauer-Arloth, J. et al.: DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning. PLoS Comput. Biol. 16:e1007616 (2020)
9.
Raimundez-Alvarez, E. et al.: Model-based analysis of response and resistance factors of cetuximab treatment in gastric cancer cell lines. PLoS Comput. Biol. 16:e1007147 (2020)
10.
Münch, K.* ; Münch, R.* ; Biedendieck, R.* ; Jahn, D.* & Müller, J.: Evolutionary model for the unequal segregation of high copy plasmids. PLoS Comput. Biol. 15:e1006724 (2019)
11.
Blazquez-Navarro, A.* et al.: Differential T cell response against BK virus regulatory and structural antigens: A viral dynamics modelling approach. PLoS Comput. Biol. 14:e1005998 (2018)
12.
Fröhlich, F. ; Kaltenbacher, B.* ; Theis, F.J. & Hasenauer, J.: Scalable parameter estimation for genome-scale biochemical reaction networks. PLoS Comput. Biol. 13:e1005331 (2017)
13.
Lepik, K.* et al.: C-reactive protein upregulates the whole blood expression of CD59 - an integrative analysis. PLoS Comput. Biol. 13:e1005766 (2017)
14.
Rueedi, R.* et al.: Metabomatching: Using genetic association to identify metabolites in proton NMR spectroscopy. PLoS Comput. Biol. 13, e1005839:e1005839 (2017)
15.
Fröhlich, F. et al.: Inference for stochastic chemical kinetics using moment equations and system size expansion. PLoS Comput. Biol. 12:e1005030 (2016)
16.
Jagiella, N. ; Müller, B.* ; Müller, M.* ; Vignon-Clementel, I.E.* & Drasdo, D.*: Inferring growth control mechanisms in growing multi-cellular spheroids of NSCLC cells from spatial-temporal image data. PLoS Comput. Biol. 12:e1004412 (2016)
17.
Lechner, M.* ; Schwarz, M. ; Opitz, M.* & Frey, E.*: Hierarchical post-transcriptional regulation of colicin E2 expression in Escherichia coli. PLoS Comput. Biol. 12:e1005243 (2016)
18.
Prinz, J. ; Vogt, I. ; Adornetto, G. & Campillos, M.J.: A novel drug-mouse phenotypic similarity method detects molecular determinants of drug effects. PLoS Comput. Biol. 12, DOI: 10.1371/journal.pcbi.1005111 (2016)
19.
Hoffmann, T.* ; Krackhardt, A.M. & Antes, I.*: Quantitative analysis of the association angle between T-cell receptor Vα/Vβ domains reveals important features for epitope recognition. PLoS Comput. Biol. 11:e1004244 (2015)
20.
Hasenauer, J. ; Hasenauer, C.* ; Hucho, T.* & Theis, F.J.: ODE constrained mixture modelling: A method for unraveling subpopulation structures and dynamics. PLoS Comput. Biol. 10:e1003686 (2014)