Difference between revisions of "Literature Studies"

(Imputation methods for missing values)
(Preprocessing high-throughput data)
Line 185: Line 185:
 
''' 2014 '''</br>
 
''' 2014 '''</br>
 
* [[Normalyzer: A Tool for Rapid Evaluation of Normalization Methods for Omics Data Sets]]
 
* [[Normalyzer: A Tool for Rapid Evaluation of Normalization Methods for Omics Data Sets]]
 +
''' 2016 '''</br>
 +
* [[Chemometric methods in data processing of mass spectrometry-based metabolomics: A review]]

Revision as of 09:57, 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

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

2016