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1.
Hedrich, N.L.* ; Schulz, E. ; Hall-McMaster, S.* & Schuck, N.W.*: An inductive bias for slowly changing features in human reinforcement learning. PLoS Comput. Biol. 20, 30:e1012568 (2024)
2.
Klinger, B.* et al.: Quantitative modeling of signaling in aggressive B cell lymphoma unveils conserved core network. PLoS Comput. Biol. 20:e1012488 (2024)
3.
Lang, P.F.* ; Penas, D.R.* ; Banga, J.R.* ; Weindl, D. & Novak, B.*: Reusable rule-based cell cycle model explains compartment-resolved dynamics of 16 observables in RPE-1 cells. PLoS Comput. Biol. 20:e1011151 (2024)
4.
Wang, Z.* ; Hasenauer, J. & Schälte, Y.: Missing data in amortized simulation-based neural posterior estimation. PLoS Comput. Biol. 20:e1012184 (2024)
5.
Zoller, H.* ; Garcia Perez, C. ; Betel Geijo Fernández, J.* & zu Castell, W.: Measuring and understanding information storage and transfer in a simulated human gut microbiome. PLoS Comput. Biol. 20:e1012359 (2024)
6.
Huang, S.* ; Alier, E. ; Kilbertus, N. & Pfister, N.*: Supervised learning and model analysis with compositional data. PLoS Comput. Biol. 19:e1011240 (2023)
7.
Lakrisenko, P. et al.: Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks. PLoS Comput. Biol. 19:e1010783 (2023)
8.
Roca-Martínez, J.* ; Dhondge, H.* ; Sattler, M. & Vranken, W.F.*: Deciphering the RRM-RNA recognition code: A computational analysis. PLoS Comput. Biol. 19:e1010859 (2023)
9.
Simonetto, C. ; Mansmann, U.* & Kaiser, J.C.: Shape-specific characterization of colorectal adenoma growth and transition to cancer with stochastic cell-based models. PLoS Comput. Biol. 19:e1010831 (2023)
10.
Ullmann, T.* ; Peschel, S. ; Finger, P.* ; Müller, C.L. & Boulesteix, A.L.*: Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering. PLoS Comput. Biol. 19:e1010820 (2023)
11.
Fiorentino, J. & Scialdone, A.: The role of cell geometry and cell-cell communication in gradient sensing. PLoS Comput. Biol. 18:e1009552 (2022)
12.
Schuh, L. et al.: Altered expression response upon repeated gene repression in single yeast cells. PLoS Comput. Biol. 18:e1010640 (2022)
13.
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)
14.
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)
15.
Schmiester, L. et al.: PEtab-Interoperable specification of parameter estimation problems in systems biology. PLoS Comput. Biol. 17:e1008646 (2021)
16.
Dorigatti, E. & Schubert, B.: Graph-theoretical formulation of the generalized epitope-based vaccine design problem. PLoS Comput. Biol. 16:e1008237 (2020)
17.
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)
18.
Knauer-Arloth, J. et al.: DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning. PLoS Comput. Biol. 16:e1007616 (2020)
19.
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)
20.
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)
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