scholarly journals Model-based gene set analysis for Bioconductor

2011 ◽  
Vol 27 (13) ◽  
pp. 1882-1883 ◽  
Author(s):  
S. Bauer ◽  
P. N. Robinson ◽  
J. Gagneur
Keyword(s):  
Gene Set Analysis ◽  
Gene Set ◽  
Model Based

scholarly journals GOing Bayesian: model-based gene set analysis of genome-scale data

2010 ◽  
Vol 38 (11) ◽  
pp. 3523-3532 ◽  
Author(s):  
Sebastian Bauer ◽  
Julien Gagneur ◽  
Peter N. Robinson
Keyword(s):  
Bayesian Model ◽  
Gene Set Analysis ◽  
Gene Set ◽  
Model Based ◽  
Genome Scale ◽  
Scale Data

scholarly journals A multi-functional analyzer uses parameter constraints to improve the efficiency of model-based gene-set analysis

2015 ◽  
Vol 9 (1) ◽  
pp. 225-246 ◽  
Author(s):  
Zhishi Wang ◽  
Qiuling He ◽  
Bret Larget ◽  
Michael A. Newton
Keyword(s):  
Gene Set Analysis ◽  
Gene Set ◽  
Model Based ◽  
Parameter Constraints

scholarly journals WTFgenes: What's The Function of these genes? Static sites for model-based gene set analysis

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 423
Author(s):  
Christopher J. Mungall ◽  
Ian H. Holmes
Keyword(s):  
Gene Ontology ◽  
Hypergeometric Distribution ◽  
Gene Set Analysis ◽  
Web Browser ◽  
Gene Set ◽  
Model Based ◽  
Common Technique

A common technique for interpreting experimentally-identified lists of genes is to look for enrichment of genes associated with particular ontology terms. The most common test uses the hypergeometric distribution; more recently, a model-based test was proposed. These approaches must typically be run using downloaded software, or on a server. We develop a collapsed likelihood for model-based gene set analysis and present WTFgenes, an implementation of both hypergeometric and model-based approaches, that can be published as a static site with computation run in JavaScript on the user's web browser client. Apart from hosting files, zero server resources are required: the site can (for example) be served directly from Amazon S3 or GitHub Pages. A C++11 implementation yielding identical results runs roughly twice as fast as the JavaScript version. WTFgenes is available from https://github.com/evoldoers/wtfgenes under the BSD3 license. A demonstration for the Gene Ontology is usable at https://evoldoers.github.io/wtfgo.

scholarly journals WTFgenes: What's The Function of these genes? Static sites for model-based gene set analysis

2017 ◽  
Author(s):  
Christopher J. Mungall ◽  
Ian H. Holmes
Keyword(s):  
Gene Ontology ◽  
Hypergeometric Distribution ◽  
Gene Set Analysis ◽  
Web Browser ◽  
Gene Set ◽  
Model Based ◽  
Link Type ◽  
Common Technique

AbstractA common technique for interpreting experimentally-identified lists of genes is to look for enrichment of genes associated to particular ontology terms. The most common technique uses the hypergeometric distribution; more recently, a model-based approach was proposed. These approaches must typically be run using downloaded software, or on a server. We develop a collapsed likelihood for model-based gene set analysis and present WTFgenes, an implementation of both hypergeometric and model-based approaches, that can be published as a static site with computation run in JavaScript on the user's web browser client. Apart from hosting files, zero server resources are required: the site can (for example) be served directly from Amazon S3 or GitHub Pages. A C++11 implementation yielding identical results runs roughly twice as fast as the JavaScript version. WTFgenes is available from https://github.com/evoldoers/wtfgenes under the BSD3 license. A demonstration for the Gene Ontology is usable at https://evoldoers.github.io/wtfgo. Contact: Ian Holmes [email protected].

scholarly journals DSigDB: drug signatures database for gene set analysis: Fig. 1.

2015 ◽  
Vol 31 (18) ◽  
pp. 3069-3071 ◽  
Author(s):  
Minjae Yoo ◽  
Jimin Shin ◽  
Jihye Kim ◽  
Karen A. Ryall ◽  
Kyubum Lee ◽  
...  
Keyword(s):  
Gene Set Analysis ◽  
Gene Set

scholarly journals Popularity and performance of bioinformatics software: the case of gene set analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Chengshu Xie ◽  
Shaurya Jauhari ◽  
Antonio Mora
Keyword(s):  
Comprehensive Evaluation ◽  
Gene Set Analysis ◽  
Tool Selection ◽  
Functional Interpretation ◽  
Selection Procedures ◽  
Gene Set ◽  
And Performance ◽  
Reported Data ◽  
Performance Results

Abstract Background Gene Set Analysis (GSA) is arguably the method of choice for the functional interpretation of omics results. The following paper explores the popularity and the performance of all the GSA methodologies and software published during the 20 years since its inception. "Popularity" is estimated according to each paper's citation counts, while "performance" is based on a comprehensive evaluation of the validation strategies used by papers in the field, as well as the consolidated results from the existing benchmark studies. Results Regarding popularity, data is collected into an online open database ("GSARefDB") which allows browsing bibliographic and method-descriptive information from 503 GSA paper references; regarding performance, we introduce a repository of jupyter workflows and shiny apps for automated benchmarking of GSA methods (“GSA-BenchmarKING”). After comparing popularity versus performance, results show discrepancies between the most popular and the best performing GSA methods. Conclusions The above-mentioned results call our attention towards the nature of the tool selection procedures followed by researchers and raise doubts regarding the quality of the functional interpretation of biological datasets in current biomedical studies. Suggestions for the future of the functional interpretation field are made, including strategies for education and discussion of GSA tools, better validation and benchmarking practices, reproducibility, and functional re-analysis of previously reported data.

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