Difference between revisions of "Literature Studies"

(Identifying differential regions (e.g. DMRs))
(Identifying differential regions (e.g. DMRs))
Line 39: Line 39:
 
''' 2015 '''
 
''' 2015 '''
 
* [[De novo identification of differentially methylated regions in the human genome]]
 
* [[De novo identification of differentially methylated regions in the human genome]]
* ( [[MethylAction: detecting differentially methylated regions that distinguish biological subtypes]] )
+
* [[MethylAction: detecting differentially methylated regions that distinguish biological subtypes]]
 
* [[metilene: Fast and sensitive calling of differentially methylated regions from bisulfite sequencing data]]
 
* [[metilene: Fast and sensitive calling of differentially methylated regions from bisulfite sequencing data]]
 
''' 2016 '''
 
''' 2016 '''

Revision as of 14:00, 27 June 2019

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)

1.2.4 Dimension reduction

2008

2015

1.3 Imputation methods for missing values

2001

2015

2018


1.4 ODE-based Modelling

2001

2008

2011

2013

2018

1.5 Omics Workflows

2015

2017

2019


1.6 Preprocessing high-throughput data

2003

2005

2006

2008

2009

2010

2011

2012

2014