Difference between revisions of "Literature Studies"
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| 2014 || Cox J || [[Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ* ]] | | 2014 || Cox J || [[Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ* ]] | ||
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− | | 2015 || || [[ | + | | 2015 || Cleary || [[Comparing Variant Call Files for Performance Benchmarkingof Next-Generation Sequencing Variant Calling Pipelines]] |
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| 2016 || Tyanova S || [[The MaxQuant computational platform for mass spectrometry–based shotgun proteomics]] | | 2016 || Tyanova S || [[The MaxQuant computational platform for mass spectrometry–based shotgun proteomics]] | ||
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| 2016 || Röst HL || [[OpenMS: a flexible open-source software platform for mass spectrometry data analysis]] | | 2016 || Röst HL || [[OpenMS: a flexible open-source software platform for mass spectrometry data analysis]] | ||
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− | | 2017 || || [[A benchmarking of workflows for detecting differential splicing and differential expression at isoform level in human RNA-seq studies]] | + | | 2017 || Merino || [[A benchmarking of workflows for detecting differential splicing and differential expression at isoform level in human RNA-seq studies]] |
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| 2018 || Välikangas T || [[A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation]] | | 2018 || Välikangas T || [[A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation]] | ||
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− | | 2019 || || [[A Systematic Evaluation of Single CellRNA-Seq Analysis Pipelines]] | + | | 2019 || Vieth || [[A Systematic Evaluation of Single CellRNA-Seq Analysis Pipelines]] |
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− | | 2019 || || [[Benchmarking workflows to assess performance and suitability of germline variant calling pipelines in clinical diagnostic assays]] | + | | 2019 || Krishnan || [[Benchmarking workflows to assess performance and suitability of germline variant calling pipelines in clinical diagnostic assays]] |
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Revision as of 16:18, 28 February 2020
Page summary |
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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. |
Contents
1 Results from Literature
1.1 Classification
Year | First Author | Title |
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2003 | Wu | Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data |
2005 | Harper | A review and comparison of classification algorithms for medical decision making |
2005 | Bellaachia | Predicting Breast Cancer Survivability Using Data Mining Techniques |
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)
Year | First Author | Title |
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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
Year | First Author | Title |
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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 |