Difference between revisions of "Literature Studies"

(Two additional papers)
(DMRfinder added)
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* [[Defiant: (DMRs: easy, fast, identification and ANnoTation) identifies differentially Methylated regions from iron-deficient rat hippocampus]]
 
* [[Defiant: (DMRs: easy, fast, identification and ANnoTation) identifies differentially Methylated regions from iron-deficient rat hippocampus]]
 
* [[DMRcaller: a versatile R/Bioconductor package for detection and visualization of differentially methylated regions in CpG and non-CpG contexts]]
 
* [[DMRcaller: a versatile R/Bioconductor package for detection and visualization of differentially methylated regions in CpG and non-CpG contexts]]
 +
* [[DMRfinder: efficiently identifying differentially methylated regions from MethylC-seq data]]
 
* [[MethCP: Differentially Methylated Region Detection with Change Point Models (bioRxiv)]]
 
* [[MethCP: Differentially Methylated Region Detection with Change Point Models (bioRxiv)]]
  

Revision as of 17:19, 25 January 2019

Page summary
Here outcomes of benchmarking studies from the literature are collected.

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
Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data 2005

2016

1.2 Feature Selection

1.2.1 Identifying different features

2006

2010

2017

1.2.2 Identifying differential regions (e.g. DMRs)

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

2013

2018


1.5 Omics Workflows

2017


1.6 Preprocessing high-throughput data

2003

2005

2006

2008

2009

2010

2011

2012

2014