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1. Literature Studies‏‎ (232 revisions)
2. A comparison of methods for quantifying prediction uncertainty in systems biology‏‎ (48 revisions)
3. Hierarchical optimization for the efficient parametrization of ODE models‏‎ (22 revisions)
4. Optimization and profile calculation of ODE models using second order adjoint sensitivity analysis‏‎ (22 revisions)
5. DMRcaller: a versatile R/Bioconductor package for detection and visualization of differentially methylated regions in CpG and non-CpG contexts‏‎ (22 revisions)
6. Lessons Learned from Quantitative Dynamical Modeling in Systems Biology‏‎ (20 revisions)
7. Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies‏‎ (19 revisions)
8. Fast derivatives of likelihood functionals for ODE based models using adjoint-state method‏‎ (17 revisions)
9. Benchmarking optimization methods for parameter estimation in large kinetic models‏‎ (16 revisions)
10. Comparing gene set analysis methods on single-nucleotide polymorphism data from Genetic Analysis Workshop 16‏‎ (16 revisions)
11. A general modular framework for gene set enrichment analysis‏‎ (15 revisions)
12. Defiant: (DMRs: easy, fast, identification and ANnoTation) identifies differentially Methylated regions from iron-deficient rat hippocampus‏‎ (15 revisions)
13. Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks‏‎ (14 revisions)
14. Performance of objective functions and optimization procedures for parameter estimation in system biology models‏‎ (13 revisions)
15. MethCP: Differentially Methylated Region Detection with Change Point Models (bioRxiv)‏‎ (12 revisions)
16. Gene set analysis methods: a systematic comparison‏‎ (11 revisions)
17. Benchmark problems for dynamic modeling of intracellular processes‏‎ (11 revisions)
18. Guidelines for Summarizing a Literature Study‏‎ (10 revisions)
19. Benchmarking Projects‏‎ (10 revisions)
20. Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics.‏‎ (9 revisions)

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