|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.
1 Results from Literature
1.2 Selection of Differential Features and Regions
1.2.1 Identifying differential features
1.2.2 Identifying differential regions (e.g. DMRs)
1.2.3 Identifying sets of features (e.g. gene set analyses)
|2009||Ackermann||A general modular framework for gene set enrichment analysis|
|2009||Tintle||Comparing gene set analysis methods on single-nucleotide polymorphism data from Genetic Analysis Workshop 16|
|2018||Mathur||Gene set analysis methods: a systematic comparison|
|2020||Geistlinger||Toward a gold standard for benchmarking gene set enrichment analysis|
1.2.4 Dimension reduction
|2008||Janecek||On the Relationship Between Feature Selection and Classification Accuracy|
|2015||Fernández-Gutiérrez||Comparing feature selection methods for highdimensional imbalanced data: identifying rheumatoid arthritis cohorts from routine data|