# Literature Studies

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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 (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

**2018**

Gene set analysis methods: a systematic comparison

#### 1.2.4 Dimension reduction

** 2008 **

** 2015 **

### 1.3 Imputation methods for missing values

** 2001 **

** 2016 **

- Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies
- Multiple imputation and analysis for high-dimensional incomplete proteomics data

** 2018 **

### 1.4 ODE-based Modelling

** 2001 **

** 2008 **

** 2011 **

** 2013 **

- Lessons Learned from Quantitative Dynamical Modeling in Systems Biology
- ODE parameter inference using adaptive gradient matching with Gaussian processes

** 2018 **

** 2020 **

#### 1.4.1 Hossein

** 2020 **

** 2019 **

- Benchmark problems for dynamic modeling of intracellular processes
- Parameter Estimation and Variable Selection for Big Systems of Linear Ordinary Differential Equations: A Matrix-Based Approach
- Benchmark problems for dynamic modeling of intracellular processes
- Continuous analogue to iterative optimization for PDE-constrained inverse problems
- Model validation in dynamic systems for time-course data with complex error structures
- Tracking for parameter and state estimation in possibly misspecified partially observed linear Ordinary Differential Equations
- Efficient computation of steady states in large-scale ODE models of biochemical reaction networks
- Statistical Model Checking-Based Analysis of Biological Networks

** 2018 **

- Hierarchical optimization for the efficient parametrization of ODE models
- Inference for differential equation models using relaxation via dynamical systems
- Identification of parameters in systems biology
- Continuous analogue to iterative optimization for PDE-constrained inverse problems
- An easy and efficient approach for testing identifiability
- Optimization and profile calculation of ODE models using second order adjoint sensitivity analysis
- Local Identifiability Analysis of NonLinear ODE Models: How to Determine All Candidate Solutions

** 2017 **

#### 1.4.2 Tim

**2017**

**2018**

#### 1.4.3 Fabian

**2019**

- Full observability and estimation of unknown inputs, states and parameters of nonlinear biological models
- A comparison of methods for quantifying prediction uncertainty in systems biology
- Parameter estimation in models of biological oscillators: an automated regularised estimation approach
- Input-dependent structural identifiability of nonlinear systems

#### 1.4.4 Lukas

**2017**

- Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks
- Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy
- Comprehensive benchmarking of Markov chain Monte Carlo methods for dynamical systems
- Data-driven reverse engineering of signaling pathways using ensembles of dynamic models

**2018**

- Optimization and uncertainty analysis of ODE models using second order adjoint sensitivity analysis
- Evaluation of Derivative-Free Optimizers for Parameter Estimation in Systems Biology

**2019**

**2020**

### 1.5 Omics Workflows

** 2015 **

** 2017 **

- A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation
- A benchmarking of workflows for detecting differential splicing and differential expression at isoform level in human RNA-seq studies

** 2019 **

- A Systematic Evaluation of Single CellRNA-Seq Analysis Pipelines
- Benchmarking workflows to assess performance and suitability of germline variant calling pipelines in clinical diagnostic assays

### 1.6 Preprocessing high-throughput data

** 2003 **

** 2005 **

- Comparison of Affymetrix GeneChip Expression Measures
- Comparison of background correction and normalization procedures for high-density oligonucleotide microarrays

** 2006 **

** 2008 **

** 2009 **

** 2010 **

- Consistency of predictive signature genes and classifiers generated using different microarray platforms
- Detecting and correcting systematic variation in large-scale RNA sequencing data
- Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments
- Normalization of RNA-seq data using factor analysis of control genes or samples

** 2011 **

** 2012 **

** 2014 **