Literature Studies

Page summary
Here outcomes of benchmarking studies from the literature are collected. 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. Use the guidelines described here.

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

2006

• Rat toxicogenomic study reveals analytical consistency across microarray platforms

2010

• A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the sequencing Quality control consortium

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

2006

• Using RNA sample titrations to assess microarray platform performance and normalization techniques

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

• Consistency of predictive signature genes and classifiers generated using different microarray platforms
• Detecting and correcting systematic variation in large-scale RNA sequencing data
• Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments
• Normalization of RNA-seq data using factor analysis of control genes or samples

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