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11 Records found.
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1.
Aliee, H. & Theis, F.J.: AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution. Cell Syst. 12, 706-715.e4 (2021)
2.
Ji, Y. ; Lotfollahi, M. ; Wolf, F.A. & Theis, F.J.: Machine learning for perturbational single-cell omics. Cell Syst. 12, 522-537 (2021)
3.
Schuh, L. et al.: H4K20 methylation is differently regulated by dilution and demethylation in proliferating and cell-cycle-arrested xenopus embryos. Cell Syst. 11, 653-662.e8 (2020)
4.
Schuh, L. et al.: Gene networks with transcriptional bursting recapitulate rare transient coordinated high expression states in cancer. Cell Syst. 10, 363-378.e12 (2020)
5.
Fröhlich, F. et al.: Efficient parameter estimation enables the prediction of drug response using a mechanistic pan-cancer pathway model. Cell Syst. 7, 567-579 (2018)
6.
Loos, C. ; Möller, K.* ; Fröhlich, F. ; Hucho, T.* & Hasenauer, J.: A hierarchical, data-driven approach to modeling single-cell populations predicts latent causes of cell-to-cell variability. Cell Syst. 6, 593-603.e13 (2018)
7.
Martinez-Corral, R.* ; Raimundez-Alvarez, E. ; Lin, Y.* ; Elowitz, M.B.* & Garcia-Ojalvo, J.*: Self-amplifying pulsatile protein dynamics without positive feedback. Cell Syst. 7, 453-462 (2018)
8.
Jagiella, N. ; Rickert, D. ; Theis, F.J. & Hasenauer, J.: Parallelization and high-performance computing enables automated statistical inference of multi-scale models. Cell Syst. 4, 194–206.e9 (2017)
9.
Blasi, T. et al.: Combinatorial histone acetylation patterns are generated by motif-specific reactions. Cell Syst. 2, 49-58 (2016)
10.
Carpenter, A.* et al.: What is the key best practice for collaborating with a computational biologist? Cell Syst. 3, 7-11 (2016)
11.
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)