# Difference between revisions of "Literature Studies"

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| 2021 || Jin L || [[A comparative study of evaluating missing value imputation methods in label-free proteomics]] | | 2021 || Jin L || [[A comparative study of evaluating missing value imputation methods in label-free proteomics]] | ||

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| 2018 || Willforss J || [[NormalyzerDE: Online Tool for Improved Normalization of Omics Expression Data and High-Sensitivity Differential Expression Analysis]] | | 2018 || Willforss J || [[NormalyzerDE: Online Tool for Improved Normalization of Omics Expression Data and High-Sensitivity Differential Expression Analysis]] | ||

+ | |} | ||

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+ | |||

+ | === ODE-based Modelling === | ||

+ | {| class="wikitable sortable" | ||

+ | |- | ||

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

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

+ | |- | ||

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

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+ | | 2011 || Tashkova || [[Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis]] | ||

+ | |- | ||

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

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

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

+ | |- | ||

+ | | 2017 || Henriques || [[Data-driven reverse engineering of signaling pathways using ensembles of dynamic models]] | ||

+ | |- | ||

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

+ | |- | ||

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

+ | |- | ||

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

+ | |- | ||

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

+ | |- | ||

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

+ | |- | ||

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

+ | |- | ||

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

+ | |- | ||

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

+ | |- | ||

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

+ | |- | ||

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

+ | |- | ||

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

+ | |- | ||

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

+ | |- | ||

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

+ | |- | ||

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

+ | |- | ||

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

+ | |- | ||

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

+ | |- | ||

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

+ | |- | ||

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

|} | |} |

## Revision as of 14:36, 2 February 2021

Page summary |
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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 form the molecular biology field (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

Year | First Author | Title |
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2003 | Wu | Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data |

2005 | Bellaachia | Predicting Breast Cancer Survivability Using Data Mining Techniques |

### 1.2 Selection of Differential Features and Regions

#### 1.2.1 Identifying differential features

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

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

Year | First Author | Title |
---|---|---|

2009 | Ackermann | A general modular framework for gene set enrichment analysis |

2009 | Tintle | Comparing gene set analysis methods on single-nucleotide polymorphism data from Genetic Analysis Workshop 16 |

2018 | Mathur | Gene set analysis methods: a systematic comparison |

2020 | Geistlinger | Toward a gold standard for benchmarking gene set enrichment analysis |

#### 1.2.4 Dimension reduction

Year | First Author | Title |
---|---|---|

2008 | Janecek | On the Relationship Between Feature Selection and Classification Accuracy |

2015 | Fernández-Gutiérrez | Comparing feature selection methods for highdimensional imbalanced data: identifying rheumatoid arthritis cohorts from routine data |

### 1.3 Imputation methods for missing values

### 1.4 Omics Workflows

### 1.5 Preprocessing high-throughput data