Difference between revisions of "Literature Studies"
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* [[Lessons Learned from Quantitative Dynamical Modeling in Systems Biology]] | * [[Lessons Learned from Quantitative Dynamical Modeling in Systems Biology]] | ||
* [[ODE parameter inference using adaptive gradient matching with Gaussian processes]] | * [[ODE parameter inference using adaptive gradient matching with Gaussian processes]] | ||
| + | ''' 2017 '''</br> | ||
| + | * [https://link.springer.com/article/10.1007%2Fs00180-017-0765-8 Fast derivatives of likelihood functionals for ODE based models using adjoint-state method] | ||
| + | * [[Hierarchical optimization for the efficient parametrization of ODE models]] | ||
| + | * [[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]] | ||
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''' 2018 '''</br> | ''' 2018 '''</br> | ||
* [[Benchmarking optimization methods for parameter estimation in large kinetic models]] | * [[Benchmarking optimization methods for parameter estimation in large kinetic models]] | ||
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*[https://doi.org/10.1093/bioinformatics/bty514 Hierarchical optimization for the efficient parametrization of ODE models] | *[https://doi.org/10.1093/bioinformatics/bty514 Hierarchical optimization for the efficient parametrization of ODE models] | ||
*[https://doi.org/10.1016/j.csda.2018.05.014 Inference for differential equation models using relaxation via dynamical systems] | *[https://doi.org/10.1016/j.csda.2018.05.014 Inference for differential equation models using relaxation via dynamical systems] | ||
*[https://doi.org/10.1080/17415977.2018.1494167 Continuous analogue to iterative optimization for PDE-constrained inverse problems] | *[https://doi.org/10.1080/17415977.2018.1494167 Continuous analogue to iterative optimization for PDE-constrained inverse problems] | ||
*[https://doi.org/10.1093/bioinformatics/bty230 Optimization and profile calculation of ODE models using second order adjoint sensitivity analysis] | *[https://doi.org/10.1093/bioinformatics/bty230 Optimization and profile calculation of ODE models using second order adjoint sensitivity analysis] | ||
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* [[Performance of objective functions and optimization procedures for parameter estimation in system biology models]] | * [[Performance of objective functions and optimization procedures for parameter estimation in system biology models]] | ||
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* [[Input-dependent structural identifiability of nonlinear systems]] | * [[Input-dependent structural identifiability of nonlinear systems]] | ||
| − | '''2019''' | + | * [[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 '''</br> | ||
| + | *[https://doi.org/10.1093/bioinformatics/btz020 Benchmark problems for dynamic modeling of intracellular processes] | ||
| + | *[https://doi.org/10.1080/01621459.2017.1423074 Parameter Estimation and Variable Selection for Big Systems of Linear Ordinary Differential Equations: A Matrix-Based Approach] | ||
| + | *[https://doi.org/10.1016/j.jspi.2018.06.005 Tracking for parameter and state estimation in possibly misspecified partially observed linear Ordinary Differential Equations] | ||
| + | *[https://doi.org/10.1016/j.ifacol.2019.12.232 Efficient computation of steady states in large-scale ODE models of biochemical reaction networks] | ||
* [[Full observability and estimation of unknown inputs, states and parameters of nonlinear biological models]] | * [[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]] | * [[A comparison of methods for quantifying prediction uncertainty in systems biology]] | ||
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* [[Mini-batch optimization enables training of ODE models on large-scale datasets]] | * [[Mini-batch optimization enables training of ODE models on large-scale datasets]] | ||
* [[Scalable nonlinear programming framework for parameter estimation in dynamic biological system models]] | * [[Scalable nonlinear programming framework for parameter estimation in dynamic biological system models]] | ||
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* [[Benchmarking optimization methods for parameter estimation in large kinetic models]] | * [[Benchmarking optimization methods for parameter estimation in large kinetic models]] | ||
| − | '''2020''' | + | ''' 2020 '''</br> |
| + | * [[An application of Conditional RobustCalibration (CRC) to ordinary differential equations (ODEs) models in computational systems biology: a comparison of two sampling strategies]] | ||
* [[Efficient parameterization of large-scale dynamic models based on relative measurements]] | * [[Efficient parameterization of large-scale dynamic models based on relative measurements]] | ||
Revision as of 10:31, 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
2018
Gene set analysis methods: a systematic comparison
1.2.4 Dimension reduction
2008
2015
1.3 Imputation methods for missing values
2001
2015
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
2017
- Fast derivatives of likelihood functionals for ODE based models using adjoint-state method
- Hierarchical optimization for the efficient parametrization of ODE models
- 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
- Benchmarking optimization methods for parameter estimation in large kinetic models
- Hierarchical optimization for the efficient parametrization of ODE models
- Inference for differential equation models using relaxation via dynamical systems
- Continuous analogue to iterative optimization for PDE-constrained inverse problems
- Optimization and profile calculation of ODE models using second order adjoint sensitivity analysis
- Performance of objective functions and optimization procedures for parameter estimation in system biology models
- Input-dependent structural identifiability of nonlinear systems
- 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
- 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
- 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
- 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
- Testing structural identifiability by a simple scaling method
- Robust calibration of hierarchical population models for heterogeneous cell populations
- Mini-batch optimization enables training of ODE models on large-scale datasets
- Scalable nonlinear programming framework for parameter estimation in dynamic biological system models
- Benchmarking optimization methods for parameter estimation in large kinetic models
2020
- An application of Conditional RobustCalibration (CRC) to ordinary differential equations (ODEs) models in computational systems biology: a comparison of two sampling strategies
- Efficient parameterization of large-scale dynamic models based on relative measurements
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
2007
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
2015
- Detecting and overcoming systematic bias in high-throughput screening technologies: a comprehensive review of practical issues and methodological solutions
- Optimization of miRNA-seq data preprocessing
2016
2018
