Difference between revisions of "Literature Studies"

(Identifying sets of features (e.g. gene set analyses))
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=== Preprocessing high-throughput data===
 
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{{#simplesort:
 
''' 2003 '''</br>
 
''' 2003 '''</br>
 
* [[A comparison of normalization methods for high density oligonucleotide array data based on variance and bias ]]
 
* [[A comparison of normalization methods for high density oligonucleotide array data based on variance and bias ]]
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''' 2018 '''</br>
 
''' 2018 '''</br>
 
* [[Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening]]
 
* [[Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening]]
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Revision as of 11:37, 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

2018

1.2.4 Dimension reduction

2008

2015

1.3 Imputation methods for missing values

2001

2002

2008

2011

2014

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

{{#simplesort: 2003

2005

2006

2007

2008

2009

2010

2011

2012

2014

2015

2016

2018

}