Difference between revisions of "Literature Studies"

(Preprocessing high-throughput data)
(Hossein)
Line 102: Line 102:
 
''' 2018 '''</br>
 
''' 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>
 
''' 2017 '''</br>
*F[https://link.springer.com/article/10.1007%2Fs00180-017-0765-8 ast derivatives of likelihood functionals for ODE based models using adjoint-state method]
+
*[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>
 
'''2013 '''</br>
 
*[https://doi.org/10.1371/journal.pone.0074335 Lessons Learned from Quantitative Dynamical Modeling in Systems Biology]
 
*[https://doi.org/10.1371/journal.pone.0074335 Lessons Learned from Quantitative Dynamical Modeling in Systems Biology]

Revision as of 10:18, 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

2018

2020

1.4.1 Hossein

2019

2018

2017

2013

1.4.2 Tim

2017


2018

1.4.3 Fabian

2018

2019

1.4.4 Lukas

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