Difference between revisions of "Literature Studies"

(Preprocessing high-throughput data)
(ODE-based Modelling)
Line 87: Line 87:
 
* [[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]]
 +
 
''' 2018 '''</br>
 
''' 2018 '''</br>
 
* [[Benchmarking optimization methods for parameter estimation in large kinetic models]]
 
* [[Benchmarking optimization methods for parameter estimation in large kinetic models]]
''' 2020 '''</br>
 
* [[An application of Conditional RobustCalibration (CRC) to ordinary differential equations (ODEs) models in computational systems biology: a comparison of two sampling strategies]]
 
 
==== Hossein ====
 
 
 
''' 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]
 
''' 2018 '''</br>
 
 
*[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]
''' 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]
 
'''2013 '''</br>
 
*[https://doi.org/10.1371/journal.pone.0074335 Lessons Learned from Quantitative Dynamical Modeling in Systems Biology]
 
 
==== Tim ====
 
 
'''2017'''
 
* [[Hierarchical optimization for the efficient parametrization of ODE models]]
 
 
 
'''2018'''
 
 
 
* [[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]]
 
==== Fabian ====
 
'''2018'''
 
 
* [[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]]
Line 131: Line 117:
 
* [[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]]
 
==== Lukas ====
 
'''2017'''
 
* [[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'''
 
* [[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'''
 
 
* [[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.
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

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

2018

1.4 ODE-based Modelling

2001

2008

2011

2013

2017

2018

2019

2020

1.5 Omics Workflows

2015

2017

2019


1.6 Preprocessing high-throughput data

2003

2005

2006

2007

2008

2009

2010

2011

2012

2014

2015

2016

2018