Difference between revisions of "Literature Studies"

(Preprocessing high-throughput data)
(ODE-based Modelling)
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* [[Evaluation of Derivative-Free Optimizers for Parameter Estimation in Systems Biology]]
 
* [[Evaluation of Derivative-Free Optimizers for Parameter Estimation in Systems Biology]]
 
''' 2019 '''</br>
 
''' 2019 '''</br>
*[https://doi.org/10.1093/bioinformatics/btz020 Benchmark problems for dynamic modeling of intracellular processes]
+
* [[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]
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* [[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]
+
* [[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]
+
* [[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]]

Revision as of 10:39, 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