Literature Studies

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

• Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data

2005

• A review and comparison of classification algorithms for medical decision making

2016

• Predicting Breast Cancer Survivability Using Data Mining Techniques

1.2 Selection of Differential Features and Regions

1.2.1 Identifying differential features

2006

• Rat toxicogenomic study reveals analytical consistency across microarray platforms

2010

• A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the sequencing Quality control consortium

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

• Identification of Differentially Methylated Sites with Weak Methylation Effects

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

• DMRfinder: efficiently identifying differentially methylated regions from MethylC-seq data

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

• Gene set analysis methods: a systematic comparison

2020

• Toward a gold standard for benchmarking gene set enrichment analysis

1.2.4 Dimension reduction

2008

• On the Relationship Between Feature Selection and Classification Accuracy

2015

• Comparing feature selection methods for highdimensional imbalanced data: identifying rheumatoid arthritis cohorts from routine data

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	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
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	Dondelinger	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	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

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
2003		Preprocessing of tandem mass spectrometric data to support automatic protein identification
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
2006	Du P	Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching
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
2010	Armananzas R	Peakbin selection in mass spectrometry data using a consensus approach with estimation of distribution algorithms
2011		Affymetrix GeneChip microarray preprocessing for multivariate analyses
2011	Zhang ZM	Peak alignment using wavelet pattern matching and differential evolution
2012		A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis
2013	García-Torres M	Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data
2013	Horvatovich P	Data Preprocessing for Biomarker Discovery
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
2014	Aggio RB	Identifying and quantifying metabolites by scoring peaks of GC-MS data
2014	Cox J	Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ
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
2015	Lu J	Improved Peak Detection and Deconvolution of Native Electrospray Mass Spectra from Large Protein Complexes
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
2016	Zheng Y	An improved algorithm for peak detection in mass spectra based on continuous wavelet transform
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