Difference between revisions of "Literature Studies"
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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=== | === Preprocessing high-throughput data=== | ||
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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]] | | 2021 || Jin L || [[A comparative study of evaluating missing value imputation methods in label-free proteomics]] | ||
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|- | |- | ||
| 2020 || Li || [[Choice of library size normalization and statistical methods for differential gene expression analysis in balanced two-group comparisons for RNA-seq studies]] | | 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]] | ||
|} | |} | ||
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|- | |- | ||
| 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 === | ||
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|- | |- | ||
| 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 |
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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. 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