# Difference between revisions of "Literature Studies"

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! Year || First Author || Title | ! Year || First Author || Title | ||

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− | | 2001 || | + | | 2001 || || [[Ways to Fit a PK Model with Some Data Below the Quantification Limit]] |

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| 2008 || Balsa-Canto || [[Hybrid optimization method with general switching strategy for parameter estimation]] | | 2008 || Balsa-Canto || [[Hybrid optimization method with general switching strategy for parameter estimation]] | ||

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| 2013 || Raue || [[Lessons Learned from Quantitative Dynamical Modeling in Systems Biology]] | | 2013 || Raue || [[Lessons Learned from Quantitative Dynamical Modeling in Systems Biology]] | ||

|- | |- | ||

− | | 2013 || | + | | 2013 || || [[ODE parameter inference using adaptive gradient matching with Gaussian processes]] |

|- | |- | ||

| 2017 || Ballnus || [[Comprehensive benchmarking of Markov chain Monte Carlo methods for dynamical systems]] | | 2017 || Ballnus || [[Comprehensive benchmarking of Markov chain Monte Carlo methods for dynamical systems]] | ||

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| 2017 || Melicher || [[Fast derivatives of likelihood functionals for ODE based models using adjoint-state method]] | | 2017 || Melicher || [[Fast derivatives of likelihood functionals for ODE based models using adjoint-state method]] | ||

|- | |- | ||

− | | 2017 || | + | | 2017 || Penas || [[Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy]] |

|- | |- | ||

− | | 2017 || | + | | 2017 || Degasperi || [[Performance of objective functions and optimization procedures for parameter estimation in system biology models]] |

|- | |- | ||

− | | 2017 || | + | | 2017 || Fröhlich || [[Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks]] |

|- | |- | ||

− | | 2018 || | + | | 2018 || Schälte || [[Evaluation of Derivative-Free Optimizers for Parameter Estimation in Systems Biology]] |

|- | |- | ||

− | | 2018 || | + | | 2018 || Loos || [[Hierarchical optimization for the efficient parametrization of ODE models]] |

|- | |- | ||

− | | 2018 || | + | | 2018 || Villaverde || [[Input-dependent structural identifiability of nonlinear systems]] |

|- | |- | ||

− | | 2018 || | + | | 2018 || Stapor || [[Optimization and profile calculation of ODE models using second order adjoint sensitivity analysis]] |

|- | |- | ||

− | | 2019 || | + | | 2019 || Villaverde || [[A comparison of methods for quantifying prediction uncertainty in systems biology]] |

|- | |- | ||

− | | 2019 || | + | | 2019 || Hass || [[Benchmark problems for dynamic modeling of intracellular processes]] |

|- | |- | ||

− | | 2019 || | + | | 2019 || Villaverde || [[Benchmarking optimization methods for parameter estimation in large kinetic models]] |

|- | |- | ||

− | | 2019 || | + | | 2019 || Lines || [[Efficient computation of steady states in large-scale ODE models of biochemical reaction networks]] |

|- | |- | ||

− | | 2019 || | + | | 2019 || Villaverde || [[Full observability and estimation of unknown inputs, states and parameters of nonlinear biological models]] |

|- | |- | ||

− | | 2019 || | + | | 2019 || Stapor || [[Mini-batch optimization enables training of ODE models on large-scale datasets]] |

|- | |- | ||

− | | 2019 || | + | | 2019 || Wu || [[Parameter Estimation and Variable Selection for Big Systems of Linear Ordinary Differential Equations: A Matrix-Based Approach]] |

|- | |- | ||

− | | 2019 || | + | | 2019 || Pitt || [[Parameter estimation in models of biological oscillators: an automated regularised estimation approach]] |

|- | |- | ||

− | | 2019 || | + | | 2019 || Loos || [[Robust calibration of hierarchical population models for heterogeneous cell populations]] |

|- | |- | ||

− | | 2019 || | + | | 2019 || Shin || [[Scalable nonlinear programming framework for parameter estimation in dynamic biological system models]] |

|- | |- | ||

− | | 2019 || | + | | 2019 || Clairon || [[Tracking for parameter and state estimation in possibly misspecified partially observed linear Ordinary Differential Equations]] |

|- | |- | ||

| 2020 || Schenker || [[An application of Conditional RobustCalibration (CRC) to ordinary differential equations (ODEs) models in computational systems biology: a comparison of two sampling strategies]] | | 2020 || Schenker || [[An application of Conditional RobustCalibration (CRC) to ordinary differential equations (ODEs) models in computational systems biology: a comparison of two sampling strategies]] | ||

|- | |- | ||

− | | 2020 || | + | | 2020 || Schmiester || [[Efficient parameterization of large-scale dynamic models based on relative measurements]] |

|- | |- | ||

− | | 2020 || | + | | 2020 || Castro || [[Testing structural identifiability by a simple scaling method]] |

|} | |} | ||

## Revision as of 15:40, 25 February 2020

Page summary |
---|

Here outcomes of benchmarking studies from the literature are collected. The primary aim is a comprehensive overview about neutral benchmark studies, i.e. assessments which were performed independenty on publication of a new approach. Studies which are not neutral are put in brackets. The focus is on computational methods for analyzing experimental data (instead of comparing experimental techniques or platforms). Please extend this list by creating a new page and adding a link below. |

## Contents

## 1 Results from Literature

### 1.1 Classification

** 2003 **

** 2005 **

** 2016 **

### 1.2 Selection of Differential Features and Regions

#### 1.2.1 Identifying differential features

** 2006 **

** 2010 **

** 2017 **

- Identification of differentially expressed peptides in high-throughput proteomics data
- In-depth method assessments of differentially expressed protein detection for shotgun proteomics data with missing values
- Strategies for analyzing bisulfite sequencing data

** 2018 **

#### 1.2.2 Identifying differential regions (e.g. DMRs)

** 2015 **

- De novo identification of differentially methylated regions in the human genome
- MethylAction: detecting differentially methylated regions that distinguish biological subtypes
- metilene: Fast and sensitive calling of differentially methylated regions from bisulfite sequencing data

** 2016 **

- seqlm: an MDL based method for identifying differentially methylated regions in high density methylation array data
- Statistical methods for detecting differentially methylated regions based on MethylCap-seq data

** 2017 **

** 2018 **

- Defiant: (DMRs: easy, fast, identification and ANnoTation) identifies differentially Methylated regions from iron-deficient rat hippocampus
- DMRcaller: a versatile R/Bioconductor package for detection and visualization of differentially methylated regions in CpG and non-CpG contexts
- MethCP: Differentially Methylated Region Detection with Change Point Models (bioRxiv)

#### 1.2.3 Identifying sets of features (e.g. gene set analyses)

**2009**

- A general modular framework for gene set enrichment analysis
- Comparing gene set analysis methods on single-nucleotide polymorphism data from Genetic Analysis Workshop 16

**2018**

**2020**

#### 1.2.4 Dimension reduction

** 2008 **

** 2015 **