Difference between revisions of "Literature Studies"

(ODE-based Modelling)
(ODE-based Modelling)
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* [[ODE parameter inference using adaptive gradient matching with Gaussian processes]]
 
* [[ODE parameter inference using adaptive gradient matching with Gaussian processes]]
 
''' 2017 '''</br>
 
''' 2017 '''</br>
* [[ Fast derivatives of likelihood functionals for ODE based models using adjoint-state method]]
+
* [[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]]
 
* [[Hierarchical optimization for the efficient parametrization of ODE models]]
 
* [[Hierarchical optimization for the efficient parametrization of ODE models]]
 +
* [[Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy]]
 
* [[Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks]]
 
* [[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>

Revision as of 10:38, 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