Difference between revisions of "Literature Studies"
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=== Classification === | === Classification === | ||
− | ''' 2003 ''' | + | ''' 2003 '''</br> |
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> | ''' 2005 '''</br> | ||
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''' 2016 '''</br> | ''' 2016 '''</br> | ||
* [[Predicting Breast Cancer Survivability Using Data Mining Techniques]] | * [[Predicting Breast Cancer Survivability Using Data Mining Techniques]] | ||
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=== Feature Selection === | === Feature Selection === |
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