Difference between revisions of "Literature Studies"

(→‎Results from Literature)
(→‎Identifying differential features)
 
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== Results from Literature ==
 
== Results from Literature ==
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+
https://journals.tubitak.gov.tr/biology/issues/biy-21-45-2/biy-45-2-1-2008-8.pdf
−
=== Classification ===
 
−
{| class="wikitable sortable"
 
−
|-
 
−
! Year || First Author || Title
 
−
|-
 
−
| 2003 || Wu || [[Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data]]
 
−
|-
 
−
| 2005 || Bellaachia|| [[Predicting Breast Cancer Survivability Using Data Mining Techniques]]
 
−
|}
 
−
 
 
−
=== Selection of Differential Features and Regions ===
 
−
==== Identifying differential features ====
 
−
{| class="wikitable sortable"
 
−
|-
 
−
! Year || First Author || Title
 
−
|-
 
−
| 2006 || Guo || [[Rat toxicogenomic study reveals analytical consistency across microarray platforms]]
 
−
|-
 
−
| 2006 || Yang || [[The impact of sample imbalance on identifying differentially expressed genes]]
 
−
|-
 
−
| 2010 || Su || [[A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the sequencing Quality control consortium]]
 
−
|-
 
−
| 2014 || Ching || [[Power analysis and sample size estimation for RNA-Seq differential expression]]
 
−
|-
 
−
| 2017 || van Ooijen || [[Identification of differentially expressed peptides in high-throughput proteomics data]]
 
−
|-
 
−
| 2017 || Wang || [[In-depth method assessments of differentially expressed protein detection for shotgun proteomics data with missing values]]
 
−
|-
 
−
| 2017 || Wreczycka || [[Strategies for analyzing bisulfite sequencing data]]
 
−
|-
 
−
| 2018 || Tran || [[Identification of Differentially Methylated Sites with Weak Methylation Effects]]
 
−
|-
 
−
| 2020 || Li || [[Choice of library size normalization and statistical methods for differential gene expression analysis in balanced two-group comparisons for RNA-seq studies]]
 
−
|}
 
−
 
 
−
==== Identifying differential regions (e.g. DMRs) ====
 
−
{| class="wikitable sortable"
 
−
|-
 
−
! 2015 || Peters || [[De novo identification of differentially methylated regions in the human genome]]
 
−
|-
 
−
| 2015 || Bhasin || [[MethylAction: detecting differentially methylated regions that distinguish biological subtypes]]
 
−
|-
 
−
| 2015 || Jühling || [[metilene: Fast and sensitive calling of differentially methylated regions from bisulfite sequencing data]]
 
−
|-
 
−
| 2016 || Kolde || [[seqlm: an MDL based method for identifying differentially methylated regions in high density methylation array data]]
 
−
|-
 
−
| 2016 || Ayyala || [[Statistical methods for detecting differentially methylated regions based on MethylCap-seq data]]
 
−
|-
 
−
| 2017 || Gaspar || [[DMRfinder: efficiently identifying differentially methylated regions from MethylC-seq data]]
 
−
|-
 
−
| 2018 || Condon || [[Defiant: (DMRs: easy, fast, identification and ANnoTation) identifies differentially Methylated regions from iron-deficient rat hippocampus]]
 
−
|-
 
−
| 2018 || Catoni || [[DMRcaller: a versatile R/Bioconductor package for detection and visualization of differentially methylated regions in CpG and non-CpG contexts]]
 
−
|-
 
−
| 2018 || Gong || [[MethCP: Differentially Methylated Region Detection with Change Point Models (bioRxiv)]]
 
−
|}
 
−
 
 
−
==== Identifying sets of features (e.g. gene set analyses) ====
 
−
{| class="wikitable sortable"
 
−
|-
 
−
! Year || First Author || Title
 
−
|-
 
−
| 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]]
 
−
|}
 
−
 
 
−
==== Dimension reduction ====
 
−
 
 
−
{| class="wikitable sortable"
 
−
|-
 
−
! Year || First Author || Title
 
−
|-
 
−
| 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]]
 
−
|}
 
−
 
 
  
 
=== Preprocessing high-throughput data===
 
=== Preprocessing high-throughput data===
Line 246: Line 164:
 
|-
 
|-
 
| 2018 || O'Brien JJ || [[The effects of nonignorable missing data on label-free mass spectrometry proteomics experiments]]
 
| 2018 || O'Brien JJ || [[The effects of nonignorable missing data on label-free mass spectrometry proteomics experiments]]
 +
|-
 +
| 2019 || Gunady MK || [[scGAIN: Single Cell RNA-seq Data Imputation using Generative Adversarial Networks]]
 +
|-
 +
| 2020 || Hou W || [[A systematic evaluation of single-cell RNA-sequencing imputation methods]]
 +
|-
 +
| 2020 || Zhang L || [[Comparison of Computational Methods for Imputing Single-Cell RNA-Sequencing Data]]
 +
|-
 +
| 2021 || Steinheuer LM || [[Benchmarking scRNA-seq imputation tools with respect to network inference highlights deficits in performance at high levels of sparsity]]
 
|-
 
|-
 
| 2021 || Jin L || [[A comparative study of evaluating missing value imputation methods in label-free proteomics]]
 
| 2021 || Jin L || [[A comparative study of evaluating missing value imputation methods in label-free proteomics]]
 
|}
 
|}
 +
 +
=== Selection of Differential Features and Regions ===
 +
==== Identifying differential features ====
 +
{| class="wikitable sortable"
 +
|-
 +
! Year || First Author || Title
 +
|-
 +
| 2006 || Guo || [[Rat toxicogenomic study reveals analytical consistency across microarray platforms]]
 +
|-
 +
| 2006 || Yang || [[The impact of sample imbalance on identifying differentially expressed genes]]
 +
|-
 +
| 2010 || Su || [[A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the sequencing Quality control consortium]]
 +
|-
 +
| 2014 || Ching || [[Power analysis and sample size estimation for RNA-Seq differential expression]]
 +
|-
 +
| 2017 || van Ooijen || [[Identification of differentially expressed peptides in high-throughput proteomics data]]
 +
|-
 +
| 2017 || Wang || [[In-depth method assessments of differentially expressed protein detection for shotgun proteomics data with missing values]]
 +
|-
 +
| 2017 || Wreczycka || [[Strategies for analyzing bisulfite sequencing data]]
 +
|-
 +
| 2018 || Tran || [[Identification of Differentially Methylated Sites with Weak Methylation Effects]]
 +
|-
 +
| 2020 || Li || [[Choice of library size normalization and statistical methods for differential gene expression analysis in balanced two-group comparisons for RNA-seq studies]]
 +
|-
 +
| 2021 || Das || [[A Comprehensive Survey of Statistical Approaches for Differential Expression Analysis in Single-Cell RNA Sequencing Studies]]
 +
|}
 +
 +
==== Identifying differential regions (e.g. DMRs) ====
 +
{| class="wikitable sortable"
 +
|-
 +
! 2015 || Peters || [[De novo identification of differentially methylated regions in the human genome]]
 +
|-
 +
| 2015 || Bhasin || [[MethylAction: detecting differentially methylated regions that distinguish biological subtypes]]
 +
|-
 +
| 2015 || Jühling || [[metilene: Fast and sensitive calling of differentially methylated regions from bisulfite sequencing data]]
 +
|-
 +
| 2016 || Kolde || [[seqlm: an MDL based method for identifying differentially methylated regions in high density methylation array data]]
 +
|-
 +
| 2016 || Ayyala || [[Statistical methods for detecting differentially methylated regions based on MethylCap-seq data]]
 +
|-
 +
| 2017 || Gaspar || [[DMRfinder: efficiently identifying differentially methylated regions from MethylC-seq data]]
 +
|-
 +
| 2018 || Condon || [[Defiant: (DMRs: easy, fast, identification and ANnoTation) identifies differentially Methylated regions from iron-deficient rat hippocampus]]
 +
|-
 +
| 2018 || Catoni || [[DMRcaller: a versatile R/Bioconductor package for detection and visualization of differentially methylated regions in CpG and non-CpG contexts]]
 +
|-
 +
| 2018 || Gong || [[MethCP: Differentially Methylated Region Detection with Change Point Models (bioRxiv)]]
 +
|}
 +
 +
==== Identifying sets of features (e.g. gene set analyses) ====
 +
{| class="wikitable sortable"
 +
|-
 +
! Year || First Author || Title
 +
|-
 +
| 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]]
 +
|}
 +
 +
==== Dimension reduction ====
 +
 +
{| class="wikitable sortable"
 +
|-
 +
! Year || First Author || Title
 +
|-
 +
| 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]]
 +
|}
 +
 +
=== Classification ===
 +
{| class="wikitable sortable"
 +
|-
 +
! Year || First Author || Title
 +
|-
 +
| 2003 || Wu || [[Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data]]
 +
|-
 +
| 2005 || Bellaachia|| [[Predicting Breast Cancer Survivability Using Data Mining Techniques]]
 +
|}
 +
  
 
=== Omics Workflows ===
 
=== Omics Workflows ===
Line 280: Line 291:
 
|-
 
|-
 
| 2020 || Tang ||  [[Simultaneous Improvement in the Precision, Accuracy and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains]]
 
| 2020 || Tang ||  [[Simultaneous Improvement in the Precision, Accuracy and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains]]
 +
|-
 +
| 2021 || Dowell JA ||  [[Benchmarking Quantitative Performance in Label-Free Proteomics]]
 
|}
 
|}
  
 +
=== Microbiome & Metagenomics ===
 +
 +
{| class="wikitable sortable"
 +
|-
 +
! Year || First Author || Title
 +
|-
 +
| 2016 || D’Amore R ||  [[A comprehensive benchmarking study of protocols and sequencing platforms for 16S rRNA community profiling]]
 +
|-
 +
| 2016 || Bokulich N ||  [[mockrobiota: a public resource for microbiome bioinformatics benchmarking]]
 +
|-
 +
| 2017 || McIntyre AB ||  [[Comprehensive benchmarking and ensemble approaches for metagenomic classifiers]]
 +
|-
 +
| 2018 || Nearing JT ||  [[Denoising the Denoisers: an independent evaluation of microbiome sequence error-correction approaches]]
 +
|-
 +
| 2019 || Ye S ||  [[Benchmarking Metagenomics Tools for Taxonomic Classification]]
 +
|-
 +
| 2020 || Wang XW ||  [[Comparative study of classifiers for human microbiome data]]
 +
|-
 +
| 2020 || Calgaro M ||  [[Assessment of statistical methods from single cell, bulk RNA-seq, and metagenomics applied to microbiome data]]
 +
|-
 +
| 2020 || Seppey M ||  [[LEMMI: a continuous benchmarking platform for metagenomics classifiers]]
 +
|-
 +
| 2021 || Kubinski R ||  [[Benchmark of data processing methods and machine learning models for gut microbiome-based diagnosis of inflammatory bowel disease]]
 +
|-
 +
| 2021 ||  Lloréns-Rico V ||  [[Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases]]
 +
|-
 +
| 2021 ||  Andreu-Sánchez S ||  [[A benchmark of genetic variant calling pipelines using metagenomic short-read sequencing]]
 +
|-
 +
| 2021 ||  Cho H ||  [[Distribution-based comprehensive evaluation of methods for differential expression analysis in metatranscriptomics]]
 +
|-
 +
| 2021 ||  Parks DH ||  [[Evaluation of the microba community profiler for taxonomic profiling of metagenomic datasets from the human gut microbiome]]
 +
|-
 +
| 2021 ||  Dixit K ||  [[Benchmarking of 16S rRNA gene databases using known strain sequences]]
 +
|-
 +
| 2021 ||  Khomich M ||  [[Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods]]
 +
|-
 +
| 2022 || Nearing J ||  [[Microbiome differential abundance methods produce different results across 38 datasets]]
 +
|-
 +
| 2022 || Briscoe L ||  [[Evaluating supervised and unsupervised background noise correction in human gut microbiome data]]
 +
|-
 +
| 2024 || Marić J ||  [[Comparative analysis of metagenomic classifiers for long-read sequencing datasets]]
 +
|}
 +
 +
=== Single Cell Omics ===
 +
{| class="wikitable sortable"
 +
|-
 +
! Year || First Author || Title || Link
 +
|-
 +
| 2023 || Alaqueeli || [[Evaluating the Performance of the Generalized Linear Model (glm) R Package Using Single-Cell RNA-Sequencing Data]] || https://www.mdpi.com/2076-3417/13/20/11512
 +
|}
  
 
=== ODE-based Modelling ===
 
=== ODE-based Modelling ===
Line 337: Line 400:
 
|-
 
|-
 
| 2020 || Castro ||  [[Testing structural identifiability by a simple scaling method]]
 
| 2020 || Castro ||  [[Testing structural identifiability by a simple scaling method]]
 +
|-
 +
| 2023 || Loman ||  [[Catalyst: Fast and flexible modeling of reaction networks]]
 
|}
 
|}
 +
 +
=== AI & Deep Learning ===
 +
 +
{| class="wikitable sortable"
 +
|-
 +
! Year || First Author || Title || Link
 +
|-
 +
| 2023 || Template Author || [[Template Title]] || https://a.template.link
 +
|}
 +
 +
 +
=== Other Studies ===
 +
https://link.springer.com/article/10.1007/s00521-021-06188-z
 +
 +
https://www.diva-portal.org/smash/get/diva2:1568674/FULLTEXT01.pdf
 +
 +
https://www.sciencedirect.com/science/article/pii/S2405471221002076
 +
 +
https://www.tandfonline.com/doi/abs/10.1080/15476286.2021.1940047
 +
 +
https://escholarship.org/content/qt4091n16g/qt4091n16g.pdf

Latest revision as of 11:04, 3 April 2024

Page summary
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 form the molecular biology field (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

https://journals.tubitak.gov.tr/biology/issues/biy-21-45-2/biy-45-2-1-2008-8.pdf

1.1 Preprocessing high-throughput data

Year First Author Title
2003 Bolstad A comparison of normalization methods for high density oligonucleotide array data based on variance and bias
2003 Gentzel Preprocessing of tandem mass spectrometric data to support automatic protein identification
2005 Irizarry Comparison of Affymetrix GeneChip Expression Measures
2005 Meleth S The case for well-conducted experiments to validate statistical protocols for 2D gels: different pre-processing = different lists of significant proteins
2005 Freudenberg Comparison of background correction and normalization procedures for high-density oligonucleotide microarrays
2006 Shippy Using RNA sample titrations to assess microarray platform performance and normalization techniques
2006 Wang P Normalization regarding non-random missing values in high-throughput mass spectrometry data
2006 Du P Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching
2007 Carvalho B Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data
2007 Cannataro M MS‐Analyzer: preprocessing and data mining services for proteomics applications on the Grid
2008 Goebels Comparison of preprocessing methods for the hgU133+2 chip from Affymetrix
2009 Autio Comparison of Affymetrix data normalization methods using 6,926 experiments across five array generations
2009 Mar JC Data-driven normalization strategies for high-throughput quantitative RT-PCR
2009 Vakhrushev SY Software platform for high-throughput glycomics
2010 Fan Consistency of predictive signature genes and classifiers generated using different microarray platforms
2010 Li Detecting and correcting systematic variation in large-scale RNA sequencing data
2010 Bullard Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments
2010 Risso Normalization of RNA-seq data using factor analysis of control genes or samples
2010 Armananzas R Peakbin selection in mass spectrometry data using a consensus approach with estimation of distribution algorithms
2011 McCall Affymetrix GeneChip microarray preprocessing for multivariate analyses
2011 Zhang ZM Peak alignment using wavelet pattern matching and differential evolution
2012 Dillies A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis
2013 García-Torres M Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data
2013 Horvatovich P Bioinformatics and Statistics: LC‐MS (/MS) Data Preprocessing for Biomarker Discovery
2014 Chawade Normalyzer: A Tool for Rapid Evaluation of Normalization Methods for Omics Data Sets
2014 Zhou X Prevention, diagnosis and treatment of high-throughput sequencing data pathologies
2014 Coble JB Comparative evaluation of preprocessing freeware on chromatography/mass spectrometry data for signature discovery
2014 Aggio RB Identifying and quantifying metabolites by scoring peaks of GC-MS data
2014 Cox J Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ
2015 Caraus I Detecting and overcoming systematic bias in high-throughput screening technologies: a comprehensive review of practical issues and methodological solutions
2015 Tam S Optimization of miRNA-seq data preprocessing
2015 Rafiei A Comparison of peak‐picking workflows for untargeted liquid chromatography/high‐resolution mass spectrometry metabolomics data analysis
2015 Chawade A Data processing has major impact on the outcome of quantitative label-free LC-MS analysis
2015 Wang T A systematic study of normalization methods for Infinium 450K methylation data using whole-genome bisulfite sequencing data
2015 Lu J Improved Peak Detection and Deconvolution of Native Electrospray Mass Spectra from Large Protein Complexes
2016 Yi L Chemometric methods in data processing of mass spectrometry-based metabolomics: A review
2016 Tsuji J Evaluation of preprocessing, mapping and postprocessing algorithms for analyzing whole genome bisulfite sequencing data
2016 Li B Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis
2016 Zheng Y An improved algorithm for peak detection in mass spectra based on continuous wavelet transform
2017 Li B NOREVA: normalization and evaluation of MS-based metabolomics data
2018 Mazoure B Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening
2018 Li Z Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection
2018 Willforss J NormalyzerDE: Online Tool for Improved Normalization of Omics Expression Data and High-Sensitivity Differential Expression Analysis


1.2 Imputation methods for missing values

Year First Author Title
1996 Schenker Partially parametric techniques for multiple imputation
1999 Hastie T Imputing Missing Data for Gene Expression Arrays
2001 Troyanskaya Missing value estimation methods for DNA microarrays
2002 Engels J Imputation of missing longitudinal data: a comparison of methods
2003 Oba A Bayesian missing value estimation method for gene expression profile data
2005 Scholz Nonlinear PCA: a missing data approach
2007 Stacklies pcaMethods—a bioconductor package providing PCA methods for incomplete data
2007 Verboven Sequential imputation for missing values
2008 Shaffer GN Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes
2011 Templ Iterative stepwise regression imputation using standard and robust methods
2012 Hrydziuszko O Missing values in mass spectrometry based metabolomics: an undervalued step in the data processing pipeline
2012 Stekhoven MissForest—non-parametric missing value imputation for mixed-type data
2013 Taylor Accounting for undetected compounds in statistical analyses of mass spectrometry ‘omic studies
2013 Waljee Comparison of imputation methods for missing laboratory data in medicine
2014 Shah Comparison of Random Forest and Parametric Imputation Models for Imputing Missing Data Using MICE: A CALIBER Study
2014 Rodwell Comparison of methods for imputing limited-range variables: a simulation study
2014 Morris Tuning multiple imputation by predictive mean matching and local residual draws
2014 Doove L Recursive partitioning for missing data imputation in the presence of interaction effects
2015 Webb-Robertson BJM Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics
2016 Folch-Fortuny A Assessment of maximum likelihood PCA missing data imputation
2016 Lazar C Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies
2016 Yin X Multiple imputation and analysis for high-dimensional incomplete proteomics data
2018 Wei R Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data
2018 Poyatos R Gap-filling a spatially explicit plant trait database: comparing imputation methods and different levels of environmental information
2018 O'Brien JJ The effects of nonignorable missing data on label-free mass spectrometry proteomics experiments
2019 Gunady MK scGAIN: Single Cell RNA-seq Data Imputation using Generative Adversarial Networks
2020 Hou W A systematic evaluation of single-cell RNA-sequencing imputation methods
2020 Zhang L Comparison of Computational Methods for Imputing Single-Cell RNA-Sequencing Data
2021 Steinheuer LM Benchmarking scRNA-seq imputation tools with respect to network inference highlights deficits in performance at high levels of sparsity
2021 Jin L A comparative study of evaluating missing value imputation methods in label-free proteomics

1.3 Selection of Differential Features and Regions

1.3.1 Identifying differential features

Year First Author Title
2006 Guo Rat toxicogenomic study reveals analytical consistency across microarray platforms
2006 Yang The impact of sample imbalance on identifying differentially expressed genes
2010 Su A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the sequencing Quality control consortium
2014 Ching Power analysis and sample size estimation for RNA-Seq differential expression
2017 van Ooijen Identification of differentially expressed peptides in high-throughput proteomics data
2017 Wang In-depth method assessments of differentially expressed protein detection for shotgun proteomics data with missing values
2017 Wreczycka Strategies for analyzing bisulfite sequencing data
2018 Tran Identification of Differentially Methylated Sites with Weak Methylation Effects
2020 Li Choice of library size normalization and statistical methods for differential gene expression analysis in balanced two-group comparisons for RNA-seq studies
2021 Das A Comprehensive Survey of Statistical Approaches for Differential Expression Analysis in Single-Cell RNA Sequencing Studies

1.3.2 Identifying differential regions (e.g. DMRs)

2015 Peters De novo identification of differentially methylated regions in the human genome
2015 Bhasin MethylAction: detecting differentially methylated regions that distinguish biological subtypes
2015 Jühling metilene: Fast and sensitive calling of differentially methylated regions from bisulfite sequencing data
2016 Kolde seqlm: an MDL based method for identifying differentially methylated regions in high density methylation array data
2016 Ayyala Statistical methods for detecting differentially methylated regions based on MethylCap-seq data
2017 Gaspar DMRfinder: efficiently identifying differentially methylated regions from MethylC-seq data
2018 Condon Defiant: (DMRs: easy, fast, identification and ANnoTation) identifies differentially Methylated regions from iron-deficient rat hippocampus
2018 Catoni DMRcaller: a versatile R/Bioconductor package for detection and visualization of differentially methylated regions in CpG and non-CpG contexts
2018 Gong MethCP: Differentially Methylated Region Detection with Change Point Models (bioRxiv)

1.3.3 Identifying sets of features (e.g. gene set analyses)

Year First Author Title
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

1.3.4 Dimension reduction

Year First Author Title
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

1.4 Classification

Year First Author Title
2003 Wu Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data
2005 Bellaachia Predicting Breast Cancer Survivability Using Data Mining Techniques


1.5 Omics Workflows

Year First Author Title
2008 Neuweger H MeltDB: a software platform for the analysis and integration of metabolomics experiment data
2008 Barla A Machine learning methods for predictive proteomics
2009 Xia J MetaboAnalyst: a web server for metabolomic data analysis and interpretation
2013 Weisser H An Automated Pipeline for High-Throughput Label-Free Quantitative Proteomics
2014 Cox J Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ*
2015 Cleary Comparing Variant Call Files for Performance Benchmarkingof Next-Generation Sequencing Variant Calling Pipelines
2016 Tyanova S The MaxQuant computational platform for mass spectrometry–based shotgun proteomics
2016 Röst HL OpenMS: a flexible open-source software platform for mass spectrometry data analysis
2017 Merino A benchmarking of workflows for detecting differential splicing and differential expression at isoform level in human RNA-seq studies
2018 Välikangas T A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation
2019 Vieth A Systematic Evaluation of Single CellRNA-Seq Analysis Pipelines
2019 Krishnan Benchmarking workflows to assess performance and suitability of germline variant calling pipelines in clinical diagnostic assays
2020 Tang Simultaneous Improvement in the Precision, Accuracy and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains
2021 Dowell JA Benchmarking Quantitative Performance in Label-Free Proteomics

1.6 Microbiome & Metagenomics

Year First Author Title
2016 D’Amore R A comprehensive benchmarking study of protocols and sequencing platforms for 16S rRNA community profiling
2016 Bokulich N mockrobiota: a public resource for microbiome bioinformatics benchmarking
2017 McIntyre AB Comprehensive benchmarking and ensemble approaches for metagenomic classifiers
2018 Nearing JT Denoising the Denoisers: an independent evaluation of microbiome sequence error-correction approaches
2019 Ye S Benchmarking Metagenomics Tools for Taxonomic Classification
2020 Wang XW Comparative study of classifiers for human microbiome data
2020 Calgaro M Assessment of statistical methods from single cell, bulk RNA-seq, and metagenomics applied to microbiome data
2020 Seppey M LEMMI: a continuous benchmarking platform for metagenomics classifiers
2021 Kubinski R Benchmark of data processing methods and machine learning models for gut microbiome-based diagnosis of inflammatory bowel disease
2021 Lloréns-Rico V Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases
2021 Andreu-Sánchez S A benchmark of genetic variant calling pipelines using metagenomic short-read sequencing
2021 Cho H Distribution-based comprehensive evaluation of methods for differential expression analysis in metatranscriptomics
2021 Parks DH Evaluation of the microba community profiler for taxonomic profiling of metagenomic datasets from the human gut microbiome
2021 Dixit K Benchmarking of 16S rRNA gene databases using known strain sequences
2021 Khomich M Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods
2022 Nearing J Microbiome differential abundance methods produce different results across 38 datasets
2022 Briscoe L Evaluating supervised and unsupervised background noise correction in human gut microbiome data
2024 Marić J Comparative analysis of metagenomic classifiers for long-read sequencing datasets

1.7 Single Cell Omics

Year First Author Title Link
2023 Alaqueeli Evaluating the Performance of the Generalized Linear Model (glm) R Package Using Single-Cell RNA-Sequencing Data https://www.mdpi.com/2076-3417/13/20/11512

1.8 ODE-based Modelling

Year First Author Title
2001 Beal Ways to Fit a PK Model with Some Data Below the Quantification Limit
2008 Balsa-Canto Hybrid optimization method with general switching strategy for parameter estimation
2011 Tashkova Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis
2013 Raue Lessons Learned from Quantitative Dynamical Modeling in Systems Biology
2013 Dondelinger ODE parameter inference using adaptive gradient matching with Gaussian processes
2017 Ballnus Comprehensive benchmarking of Markov chain Monte Carlo methods for dynamical systems
2017 Henriques Data-driven reverse engineering of signaling pathways using ensembles of dynamic models
2017 Melicher Fast derivatives of likelihood functionals for ODE based models using adjoint-state method
2017 Penas Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy
2017 Degasperi Performance of objective functions and optimization procedures for parameter estimation in system biology models
2017 Fröhlich Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks
2018 Schälte Evaluation of Derivative-Free Optimizers for Parameter Estimation in Systems Biology
2018 Loos Hierarchical optimization for the efficient parametrization of ODE models
2018 Stapor Optimization and profile calculation of ODE models using second order adjoint sensitivity analysis
2019 Villaverde A comparison of methods for quantifying prediction uncertainty in systems biology
2019 Hass Benchmark problems for dynamic modeling of intracellular processes
2019 Villaverde Benchmarking optimization methods for parameter estimation in large kinetic models
2019 Lines Efficient computation of steady states in large-scale ODE models of biochemical reaction networks
2019 Stapor Mini-batch optimization enables training of ODE models on large-scale datasets
2019 Wu Parameter Estimation and Variable Selection for Big Systems of Linear Ordinary Differential Equations: A Matrix-Based Approach
2019 Pitt Parameter estimation in models of biological oscillators: an automated regularised estimation approach
2019 Loos Robust calibration of hierarchical population models for heterogeneous cell populations
2019 Clairon Tracking for parameter and state estimation in possibly misspecified partially observed linear Ordinary Differential Equations
2020 Schmiester Efficient parameterization of large-scale dynamic models based on relative measurements
2020 Castro Testing structural identifiability by a simple scaling method
2023 Loman Catalyst: Fast and flexible modeling of reaction networks

1.9 AI & Deep Learning

Year First Author Title Link
2023 Template Author Template Title https://a.template.link


1.10 Other Studies

https://link.springer.com/article/10.1007/s00521-021-06188-z

https://www.diva-portal.org/smash/get/diva2:1568674/FULLTEXT01.pdf

https://www.sciencedirect.com/science/article/pii/S2405471221002076

https://www.tandfonline.com/doi/abs/10.1080/15476286.2021.1940047

https://escholarship.org/content/qt4091n16g/qt4091n16g.pdf

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