Difference between revisions of "Literature Studies"
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| 2018 || Mazoure B || [[Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening]] | | 2018 || Mazoure B || [[Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening]] | ||
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| − | | 2018 || Li Z || Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection]] | + | | 2018 || Li Z || [[Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection]] |
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Revision as of 13:06, 25 February 2020
| Page summary |
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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
- 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
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
