Difference between revisions of "Literature Studies"

(Preprocessing high-throughput data)
(Preprocessing high-throughput data)
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| 2005 || || [[Comparison of background correction and normalization procedures for high-density oligonucleotide microarrays]]
 
| 2005 || || [[Comparison of background correction and normalization procedures for high-density oligonucleotide microarrays]]
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| 2006 || || [[Using RNA sample titrations to assess microarray platform performance and normalization techniques]]
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| 2006 || ||  [[Normalization regarding non-random missing values in high-throughput mass spectrometry data]]
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| 2007 || ||  [[Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data]]data]]
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| 2008 || ||  [[Comparison of preprocessing methods for the hgU133+2 chip from Affymetrix]]
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| 2009 || ||  [[Comparison of Affymetrix data normalization methods using 6,926 experiments across five array generations]]
 
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''' 2006 '''</br>
 
* [[Using RNA sample titrations to assess microarray platform performance and normalization techniques]]
 
* [[Normalization regarding non-random missing values in high-throughput mass spectrometry data]]
 
''' 2007 '''</br>
 
* [[Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data]]
 
''' 2008 '''</br>
 
* [[Comparison of preprocessing methods for the hgU133+2 chip from Affymetrix]]
 
''' 2009 '''</br>
 
* [[Comparison of Affymetrix data normalization methods using 6,926 experiments across five array generations]]
 
 
''' 2010 '''</br>
 
''' 2010 '''</br>
 
* [[Consistency of predictive signature genes and classifiers generated using different microarray platforms]]
 
* [[Consistency of predictive signature genes and classifiers generated using different microarray platforms]]

Revision as of 11:51, 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

Year First Author Title
2003 A comparison of normalization methods for high density oligonucleotide array data based on variance and bias
2005 Comparison of Affymetrix GeneChip Expression Measures
2005 Comparison of background correction and normalization procedures for high-density oligonucleotide microarrays
2006 Using RNA sample titrations to assess microarray platform performance and normalization techniques
2006 Normalization regarding non-random missing values in high-throughput mass spectrometry data
2007 Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array datadata]]
2008 Comparison of preprocessing methods for the hgU133+2 chip from Affymetrix
2009 Comparison of Affymetrix data normalization methods using 6,926 experiments across five array generations


2010

2011

2012

2014

2015

2016

2018