Difference between revisions of "Literature Studies"
(→Omics Workflows) |
(→Omics Workflows) |
||
| Line 152: | Line 152: | ||
=== Omics Workflows === | === Omics Workflows === | ||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
{| class="wikitable sortable" | {| class="wikitable sortable" | ||
|- | |- | ||
! Year || First Author || Title | ! Year || First Author || Title | ||
|- | |- | ||
| − | | | + | | 2015 || || [[ComparingVariant Call Files for Performance Benchmarkingof Next-Generation Sequencing Variant Calling Pipelines]] |
| − | + | |- | |
| + | | 2017 || || [[A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation]] | ||
| + | |- | ||
| + | | 2017 || || [[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]] | ||
| + | |- | ||
| + | | 2019 || || [[Benchmarking workflows to assess performance and suitability of germline variant calling pipelines in clinical diagnostic assays]] | ||
|} | |} | ||
Revision as of 12:22, 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
- Comparing gene set analysis methods on single-nucleotide polymorphism data from Genetic Analysis Workshop 16
2018
1.2.4 Dimension reduction
2008
2015
1.3 Imputation methods for missing values
1996
1999
2001
2002
2003
2005
2007
- pcaMethods—a bioconductor package providing PCA methods for incomplete data (Stacklies)
- Sequential imputation for missing values (Verboven)
2008
2011
- Iterative stepwise regression imputation using standard and robust methods (Templ)
- Missing values in mass spectrometry based metabolomics: an undervalued step in the data processing pipeline
2012
2013
- Accounting for undetected compounds in statistical analyses of mass spectrometry ‘omic studies (Taylor)
- Comparison of imputation methods for missing laboratory data in medicine (Waljee)
2014
- Comparison of Random Forest and Parametric Imputation Models for Imputing Missing Data Using MICE: A CALIBER Study (Shah)
- Comparison of methods for imputing limited-range variables: a simulation study (Rodwell)
- Tuning multiple imputation by predictive mean matching and local residual draws (Morris)
- Recursive partitioning for missing data imputation in the presence of interaction effects.
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
- Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data (Wei)
- Gap-filling a spatially explicit plant trait database: comparing imputation methods and different levels of environmental information (Poyatos)
- The effects of nonignorable missing data on label-free mass spectrometry proteomics experiments (O'Brien)
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
- Comprehensive benchmarking of Markov chain Monte Carlo methods for dynamical systems
- Data-driven reverse engineering of signaling pathways using ensembles of dynamic models
- Fast derivatives of likelihood functionals for ODE based models using adjoint-state method
- Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy
- Performance of objective functions and optimization procedures for parameter estimation in system biology models
- Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks
2018
- Evaluation of Derivative-Free Optimizers for Parameter Estimation in Systems Biology
- Hierarchical optimization for the efficient parametrization of ODE models
- Inference for differential equation models using relaxation via dynamical systems
- Input-dependent structural identifiability of nonlinear systems
- Optimization and profile calculation of ODE models using second order adjoint sensitivity analysis
2019
- A comparison of methods for quantifying prediction uncertainty in systems biology
- Benchmark problems for dynamic modeling of intracellular processes
- Benchmarking optimization methods for parameter estimation in large kinetic models
- 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
- Mini-batch optimization enables training of ODE models on large-scale datasets
- Parameter Estimation and Variable Selection for Big Systems of Linear Ordinary Differential Equations: A Matrix-Based Approach
- Parameter estimation in models of biological oscillators: an automated regularised estimation approach
- Robust calibration of hierarchical population models for heterogeneous cell populations
- Scalable nonlinear programming framework for parameter estimation in dynamic biological system models
- Testing structural identifiability by a simple scaling method
- Tracking for parameter and state estimation in possibly misspecified partially observed linear Ordinary Differential Equations
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
