Difference between revisions of "Literature Studies"
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| 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. </br> | | 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. </br> | ||
− | The focus is on computational methods for analyzing experimental data (instead of comparing experimental techniques or platforms). </br> | + | The focus is on computational methods for analyzing experimental data form the molecular biology field (instead of comparing experimental techniques or platforms). </br> |
Please extend this list by creating a new page and adding a link below. </br> | Please extend this list by creating a new page and adding a link below. </br> | ||
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== Results from Literature == | == Results from Literature == | ||
+ | https://journals.tubitak.gov.tr/biology/issues/biy-21-45-2/biy-45-2-1-2008-8.pdf | ||
− | === | + | === Preprocessing high-throughput data=== |
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{| class="wikitable sortable" | {| class="wikitable sortable" | ||
|- | |- | ||
! Year || First Author || Title | ! Year || First Author || Title | ||
+ | |- 1999 || Perkins DN || [[Probability-based protein identification by searching sequence databases using mass spectrometry data]] | ||
+ | |- | ||
+ | | 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]] | ||
|} | |} | ||
+ | |||
=== Imputation methods for missing values === | === Imputation methods for missing values === | ||
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|- | |- | ||
| 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]] | ||
|} | |} | ||
− | === | + | === Selection of Differential Features and Regions === |
+ | ==== Identifying differential features ==== | ||
{| class="wikitable sortable" | {| class="wikitable sortable" | ||
|- | |- | ||
! Year || First Author || Title | ! 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 || | + | | 2017 || Gaspar || [[DMRfinder: efficiently identifying differentially methylated regions from MethylC-seq data]] |
|- | |- | ||
− | | 2018 || | + | | 2018 || Condon || [[Defiant: (DMRs: easy, fast, identification and ANnoTation) identifies differentially Methylated regions from iron-deficient rat hippocampus]] |
|- | |- | ||
− | | 2018 || | + | | 2018 || Catoni || [[DMRcaller: a versatile R/Bioconductor package for detection and visualization of differentially methylated regions in CpG and non-CpG contexts]] |
|- | |- | ||
− | | 2018 || | + | | 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 201: | Line 276: | ||
| 2014 || Cox J || [[Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ* ]] | | 2014 || Cox J || [[Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ* ]] | ||
|- | |- | ||
− | | 2015 || || [[ | + | | 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 || Tyanova S || [[The MaxQuant computational platform for mass spectrometry–based shotgun proteomics]] | ||
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| 2016 || Röst HL || [[OpenMS: a flexible open-source software platform for mass spectrometry data analysis]] | | 2016 || Röst HL || [[OpenMS: a flexible open-source software platform for mass spectrometry data analysis]] | ||
|- | |- | ||
− | | 2017 || || [[A benchmarking of workflows for detecting differential splicing and differential expression at isoform level in human RNA-seq studies]] | + | | 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]] | | 2018 || Välikangas T || [[A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation]] | ||
|- | |- | ||
− | | 2019 || || [[A Systematic Evaluation of Single CellRNA-Seq Analysis Pipelines]] | + | | 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]] | ||
|} | |} | ||
− | === | + | === Microbiome & Metagenomics === |
+ | |||
{| class="wikitable sortable" | {| class="wikitable sortable" | ||
|- | |- | ||
! Year || First Author || Title | ! 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 === | ||
+ | {| class="wikitable sortable" | ||
|- | |- | ||
− | | | + | ! 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]] |
+ | |} | ||
+ | |||
+ | === 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. |
Contents
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
1.2 Imputation methods for missing values
1.3 Selection of Differential Features and Regions
1.3.1 Identifying differential features
1.3.2 Identifying differential regions (e.g. DMRs)
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
1.6 Microbiome & Metagenomics
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
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