Difference between revisions of "Literature Studies"

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! Year || First Author || Title
 
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| 2001 || Schenker || [[Ways to Fit a PK Model with Some Data Below the Quantification Limit]]
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| 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]]
 
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|-
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| 2013 || Schenker ||  [[ODE parameter inference using adaptive gradient matching with Gaussian processes]]
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| 2013 || ||  [[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 || 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]]
 
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|-
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| 2017 || Schenker ||  [[Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy]]
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| 2017 || Penas ||  [[Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy]]
 
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| 2017 || Schenker ||  [[Performance of objective functions and optimization procedures for parameter estimation in system biology models]]
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| 2017 || Degasperi ||  [[Performance of objective functions and optimization procedures for parameter estimation in system biology models]]
 
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| 2017 || Schenker ||  [[Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks]]
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| 2017 || Fröhlich ||  [[Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks]]
 
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| 2018 || Schenker ||  [[Evaluation of Derivative-Free Optimizers for Parameter Estimation in Systems Biology]]
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| 2018 || Schälte ||  [[Evaluation of Derivative-Free Optimizers for Parameter Estimation in Systems Biology]]
 
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|-
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| 2018 || Schenker || [[Hierarchical optimization for the efficient parametrization of ODE models]]
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| 2018 || Loos || [[Hierarchical optimization for the efficient parametrization of ODE models]]
 
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|-
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| 2018 || Schenker || [[Input-dependent structural identifiability of nonlinear systems]]
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| 2018 || Villaverde || [[Input-dependent structural identifiability of nonlinear systems]]
 
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|-
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| 2018 || Schenker || [[Optimization and profile calculation of ODE models using second order adjoint sensitivity analysis]]
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| 2018 || Stapor || [[Optimization and profile calculation of ODE models using second order adjoint sensitivity analysis]]
 
|-
 
|-
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| 2019 || Schenker || [[A comparison of methods for quantifying prediction uncertainty in systems biology]]
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| 2019 || Villaverde || [[A comparison of methods for quantifying prediction uncertainty in systems biology]]
 
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| 2019 || Schenker || [[Benchmark problems for dynamic modeling of intracellular processes]]
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| 2019 || Hass || [[Benchmark problems for dynamic modeling of intracellular processes]]
 
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|-
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| 2019 || Schenker || [[Benchmarking optimization methods for parameter estimation in large kinetic models]]
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| 2019 || Villaverde || [[Benchmarking optimization methods for parameter estimation in large kinetic models]]
 
|-
 
|-
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| 2019 || Schenker || [[Efficient computation of steady states in large-scale ODE models of biochemical reaction networks]]
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| 2019 || Lines || [[Efficient computation of steady states in large-scale ODE models of biochemical reaction networks]]
 
|-
 
|-
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| 2019 || Schenker || [[Full observability and estimation of unknown inputs, states and parameters of nonlinear biological models]]
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| 2019 || Villaverde || [[Full observability and estimation of unknown inputs, states and parameters of nonlinear biological models]]
 
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| 2019 || Schenker || [[Mini-batch optimization enables training of ODE models on large-scale datasets]]
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| 2019 || Stapor || [[Mini-batch optimization enables training of ODE models on large-scale datasets]]
 
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| 2019 || Schenker || [[Parameter Estimation and Variable Selection for Big Systems of Linear Ordinary Differential Equations: A Matrix-Based Approach]]
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| 2019 || Wu || [[Parameter Estimation and Variable Selection for Big Systems of Linear Ordinary Differential Equations: A Matrix-Based Approach]]
 
|-
 
|-
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| 2019 || Schenker || [[Parameter estimation in models of biological oscillators: an automated regularised estimation approach]]
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| 2019 || Pitt || [[Parameter estimation in models of biological oscillators: an automated regularised estimation approach]]
 
|-
 
|-
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| 2019 || Schenker || [[Robust calibration of hierarchical population models for heterogeneous cell populations]]
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| 2019 || Loos || [[Robust calibration of hierarchical population models for heterogeneous cell populations]]
 
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|-
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| 2019 || Schenker || [[Scalable nonlinear programming framework for parameter estimation in dynamic biological system models]]
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| 2019 || Shin || [[Scalable nonlinear programming framework for parameter estimation in dynamic biological system models]]
 
|-
 
|-
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| 2019 || Schenker || [[Tracking for parameter and state estimation in possibly misspecified partially observed linear Ordinary Differential Equations]]
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| 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 || Schenker ||  [[Efficient parameterization of large-scale dynamic models based on relative measurements]]
+
| 2020 || Schmiester ||  [[Efficient parameterization of large-scale dynamic models based on relative measurements]]
 
|-
 
|-
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| 2020 || Schenker ||  [[Testing structural identifiability by a simple scaling method]]
+
| 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.
Use the guidelines described here.

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

2018

1.2.2 Identifying differential regions (e.g. DMRs)

2015

2016

2017

2018

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

2009

2018

2020

1.2.4 Dimension reduction

2008

2015

1.3 Imputation methods for missing values

Year First Author Title
1996 Schenker Partially parametric techniques for multiple imputation
1999 Hastie T Imputing Missing Data for Gene Expression Arrays
2001 Troyanskaya Missing value estimation methods for DNA microarrays
2002 Engels J Imputation of missing longitudinal data: a comparison of methods
2003 Oba A Bayesian missing value estimation method for gene expression profile data
2005 Scholz Nonlinear PCA: a missing data approach
2007 Stacklies pcaMethods—a bioconductor package providing PCA methods for incomplete data
2007 Verboven Sequential imputation for missing values
2008 Shaffer GN Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes
2011 Templ Iterative stepwise regression imputation using standard and robust methods
2012 Hrydziuszko O Missing values in mass spectrometry based metabolomics: an undervalued step in the data processing pipeline
2012 Stekhoven MissForest—non-parametric missing value imputation for mixed-type data
2013 Taylor Accounting for undetected compounds in statistical analyses of mass spectrometry ‘omic studies
2013 Waljee Comparison of imputation methods for missing laboratory data in medicine
2014 Shah Comparison of Random Forest and Parametric Imputation Models for Imputing Missing Data Using MICE: A CALIBER Study
2014 Rodwell Comparison of methods for imputing limited-range variables: a simulation study
2014 Morris Tuning multiple imputation by predictive mean matching and local residual draws
2014 Doove L Recursive partitioning for missing data imputation in the presence of interaction effects
2015 Webb-Robertson BJM Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics
2016 Folch-Fortuny A Assessment of maximum likelihood PCA missing data imputation
2016 Lazar C Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies
2016 Yin X Multiple imputation and analysis for high-dimensional incomplete proteomics data
2018 Wei R Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data
2018 Poyatos R Gap-filling a spatially explicit plant trait database: comparing imputation methods and different levels of environmental information
2018 O'Brien JJ The effects of nonignorable missing data on label-free mass spectrometry proteomics experiments

1.4 ODE-based Modelling

Year First Author Title
2001 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
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
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 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 Villaverde Input-dependent structural identifiability of nonlinear systems
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 Villaverde Full observability and estimation of unknown inputs, states and parameters of nonlinear biological models
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 Shin Scalable nonlinear programming framework for parameter estimation in dynamic biological system models
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 Schmiester Efficient parameterization of large-scale dynamic models based on relative measurements
2020 Castro Testing structural identifiability by a simple scaling method

1.5 Omics Workflows

Year First Author Title
2008 Neuweger H MeltDB: a software platform for the analysis and integration of metabolomics experiment data
2008 Barla A Machine learning methods for predictive proteomics
2009 Xia J MetaboAnalyst: a web server for metabolomic data analysis and interpretation
2013 Weisser H An Automated Pipeline for High-Throughput Label-Free Quantitative Proteomics
2014 Cox J Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ*
2015 ComparingVariant Call Files for Performance Benchmarkingof Next-Generation Sequencing Variant Calling Pipelines
2016 Tyanova S The MaxQuant computational platform for mass spectrometry–based shotgun proteomics
2016 Röst HL OpenMS: a flexible open-source software platform for mass spectrometry data analysis
2017 A benchmarking of workflows for detecting differential splicing and differential expression at isoform level in human RNA-seq studies
2018 Välikangas T A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation
2019 A Systematic Evaluation of Single CellRNA-Seq Analysis Pipelines
2019 Benchmarking workflows to assess performance and suitability of germline variant calling pipelines in clinical diagnostic assays

1.6 Preprocessing high-throughput data

Year First Author Title
2003 A comparison of normalization methods for high density oligonucleotide array data based on variance and bias
2005 Comparison of Affymetrix GeneChip Expression Measures
2005 Meleth S The case for well-conducted experiments to validate statistical protocols for 2D gels: different pre-processing = different lists of significant proteins
2005 Comparison of background correction and normalization procedures for high-density oligonucleotide microarrays
2006 Using RNA sample titrations to assess microarray platform performance and normalization techniques
2006 Wang P Normalization regarding non-random missing values in high-throughput mass spectrometry data
2007 Carvalho B Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data
2007 Cannataro M MS‐Analyzer: preprocessing and data mining services for proteomics applications on the Grid
2008 Comparison of preprocessing methods for the hgU133+2 chip from Affymetrix
2009 Comparison of Affymetrix data normalization methods using 6,926 experiments across five array generations
2009 Mar JC Data-driven normalization strategies for high-throughput quantitative RT-PCR
2009 Vakhrushev SY Software platform for high-throughput glycomics
2010 Consistency of predictive signature genes and classifiers generated using different microarray platforms
2010 Detecting and correcting systematic variation in large-scale RNA sequencing data
2010 Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments
2010 Normalization of RNA-seq data using factor analysis of control genes or samples
2011 Affymetrix GeneChip microarray preprocessing for multivariate analyses
2012 A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis
2014 Normalyzer: A Tool for Rapid Evaluation of Normalization Methods for Omics Data Sets
2014 Zhou X Prevention, diagnosis and treatment of high-throughput sequencing data pathologies
2014 Coble JB Comparative evaluation of preprocessing freeware on chromatography/mass spectrometry data for signature discovery
2015 Caraus I Detecting and overcoming systematic bias in high-throughput screening technologies: a comprehensive review of practical issues and methodological solutions
2015 Tam S Optimization of miRNA-seq data preprocessing
2015 Rafiei A Comparison of peak‐picking workflows for untargeted liquid chromatography/high‐resolution mass spectrometry metabolomics data analysis
2015 Chawade A Data processing has major impact on the outcome of quantitative label-free LC-MS analysis
2015 Wang T A systematic study of normalization methods for Infinium 450K methylation data using whole-genome bisulfite sequencing data
2016 Yi L Chemometric methods in data processing of mass spectrometry-based metabolomics: A review
2016 Tsuji J Evaluation of preprocessing, mapping and postprocessing algorithms for analyzing whole genome bisulfite sequencing data
2016 Li B Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis
2017 Li B NOREVA: normalization and evaluation of MS-based metabolomics data
2018 Mazoure B Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening
2018 Li Z Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection
2018 Willforss J NormalyzerDE: Online Tool for Improved Normalization of Omics Expression Data and High-Sensitivity Differential Expression Analysis
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