Literature Studies

Page summary
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.

1 Results from Literature

1.1 Classification

2003
Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data 2005

• A review and comparison of classification algorithms for medical decision making

2016

• Predicting Breast Cancer Survivability Using Data Mining Techniques

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

• On the Relationship Between Feature Selection and Classification Accuracy

2015

• Comparing feature selection methods for highdimensional imbalanced data: identifying rheumatoid arthritis cohorts from routine data

1.3 Imputation methods for missing values

2001

• Missing value estimation methods for DNA microarrays

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

• Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data

1.4 ODE-based Modelling

2001

• Ways to Fit a PK Model with Some Data Below the Quantification Limit

2008

• Hybrid optimization method with general switching strategy for parameter estimation

2013

• Lessons Learned from Quantitative Dynamical Modeling in Systems Biology
• ODE parameter inference using adaptive gradient matching with Gaussian processes

2018

• Benchmarking optimization methods for parameter estimation in large kinetic models

1.5 Omics Workflows

2017

• A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation

1.6 Preprocessing high-throughput data

2003

• A comparison of normalization methods for high density oligonucleotide array data based on variance and bias

2005

• Comparison of Affymetrix GeneChip Expression Measures
• Comparison of background correction and normalization procedures for high-density oligonucleotide microarrays

2008

• Comparison of preprocessing methods for the hgU133+2 chip from Affymetrix

2009

• Comparison of Affymetrix data normalization methods using 6,926 experiments across five array generations

2010

• Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments

2011

• Affymetrix GeneChip microarray preprocessing for multivariate analyses

2012

• A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis

2014

• Normalyzer: A Tool for Rapid Evaluation of Normalization Methods for Omics Data Sets