Difference between revisions of "Literature Studies"
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| Line 8: | Line 8: | ||
=== Classification === | === Classification === | ||
| − | + | ''' 2003 ''' | |
Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data | Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data | ||
| − | + | ''' 2005 '''</br> | |
* [[A review and comparison of classification algorithms for medical decision making]] | * [[A review and comparison of classification algorithms for medical decision making]] | ||
| − | + | ''' 2016 '''</br> | |
* [[Predicting Breast Cancer Survivability Using Data Mining Techniques]] | * [[Predicting Breast Cancer Survivability Using Data Mining Techniques]] | ||
| Line 19: | Line 19: | ||
=== Feature Selection === | === Feature Selection === | ||
==== Identifying differences ==== | ==== Identifying differences ==== | ||
| − | + | ''' 2017 '''</br> | |
* [[Identification of differentially expressed peptides in high-throughput proteomics data]] | * [[Identification of differentially expressed peptides in high-throughput proteomics data]] | ||
| − | * [[In-depth method assessments of | + | * [[In-depth method assessments of di?erentially expressed protein detection for shotgun proteomics data with missing values]] |
==== Dimension reduction ==== | ==== Dimension reduction ==== | ||
| − | + | ''' 2008 '''</br> | |
* [[On the Relationship Between Feature Selection and Classification Accuracy]] | * [[On the Relationship Between Feature Selection and Classification Accuracy]] | ||
| − | + | ''' 2015 '''</br> | |
* [[Comparing feature selection methods for highdimensional imbalanced data: identifying rheumatoid arthritis cohorts from routine data]] | * [[Comparing feature selection methods for highdimensional imbalanced data: identifying rheumatoid arthritis cohorts from routine data]] | ||
=== Imputation methods for missing values === | === Imputation methods for missing values === | ||
| − | + | ''' 2001 '''</br> | |
* [[Missing value estimation methods for DNA microarrays]] | * [[Missing value estimation methods for DNA microarrays]] | ||
| − | + | ''' 2015 '''</br> | |
* [[Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies]] | * [[Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies]] | ||
* [[Multiple imputation and analysis for high-dimensional incomplete proteomics data]] | * [[Multiple imputation and analysis for high-dimensional incomplete proteomics data]] | ||
| − | + | ''' 2018 '''</br> | |
* [[Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data]] | * [[Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data]] | ||
| Line 41: | Line 41: | ||
=== ODE-based Modelling === | === ODE-based Modelling === | ||
| − | + | ''' 2001 '''</br> | |
* [[Ways to Fit a PK Model with Some Data Below the Quantification Limit]] | * [[Ways to Fit a PK Model with Some Data Below the Quantification Limit]] | ||
| − | + | ''' 2008 '''</br> | |
* [[Hybrid optimization method with general switching strategy for parameter estimation]] | * [[Hybrid optimization method with general switching strategy for parameter estimation]] | ||
| − | + | ''' 2013 '''</br> | |
* [[Lessons Learned from Quantitative Dynamical Modeling in Systems Biology]] | * [[Lessons Learned from Quantitative Dynamical Modeling in Systems Biology]] | ||
* [[ODE parameter inference using adaptive gradient matching with Gaussian processes]] | * [[ODE parameter inference using adaptive gradient matching with Gaussian processes]] | ||
| − | + | ''' 2018 '''</br> | |
* [[Benchmarking optimization methods for parameter estimation in large kinetic models]] | * [[Benchmarking optimization methods for parameter estimation in large kinetic models]] | ||
| Line 54: | Line 54: | ||
=== Omics Workflows === | === Omics Workflows === | ||
| − | + | ''' 2017 '''</br> | |
* [[A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation]] | * [[A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation]] | ||
| Line 60: | Line 60: | ||
=== Preprocessing high-throughput data=== | === Preprocessing high-throughput data=== | ||
| − | + | ''' 2009 '''</br> | |
* [[Comparison of Affymetrix data normalization methods using 6,926 experiments across five array generations]] | * [[Comparison of Affymetrix data normalization methods using 6,926 experiments across five array generations]] | ||
| − | + | ''' 2010 '''</br> | |
* [[Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments]] | * [[Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments]] | ||
| − | + | ''' 2012 '''</br> | |
* [[A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis]] | * [[A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis]] | ||
| − | + | ''' 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]] | ||
Revision as of 13:27, 9 August 2018
Here outcomes of benchmarking studies from the literature are collected. Please extend this list by creating a new page and adding a link below. Use the guidelines described here. The goal is achieving a consensus within the scientific community.
Contents
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 differences
2017
- Identification of differentially expressed peptides in high-throughput proteomics data
- In-depth method assessments of di?erentially expressed protein detection for shotgun proteomics data with missing values
1.2.2 Dimension reduction
2008
2015
1.3 Imputation methods for missing values
2001
2015
- Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies
- Multiple imputation and analysis for high-dimensional incomplete proteomics data
2018
1.4 ODE-based Modelling
2001
2008
2013
- Lessons Learned from Quantitative Dynamical Modeling in Systems Biology
- ODE parameter inference using adaptive gradient matching with Gaussian processes
2018
1.5 Omics Workflows
2017
1.6 Preprocessing high-throughput data
2009
2010
2012
2014
