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Gavin Rhys Lloyd

dblp:149/9634 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2021
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 81% Computational science and engineering · 19%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
metabolomics
0.512021
struct: an R/Bioconductor-based framework for standardized metabolomics data analysis and beyond · Bioinform. 2021
Bioinformatics and computational biology
omics data analysis
0.112021
struct: an R/Bioconductor-based framework for standardized metabolomics data analysis and beyond · Bioinform. 2021
Computational science and engineering › computational reproducibility
reproducible workflow
0.112021
struct: an R/Bioconductor-based framework for standardized metabolomics data analysis and beyond · Bioinform. 2021

Methods — techniques the papers use, named apart from their topics

class-based templates · 0.5STATistics Ontology · 0.5
YearPublicationVenuePosition
2021 struct: an R/Bioconductor-based framework for standardized metabolomics data analysis and beyond
abstract
SUMMARY: Implementing and combining methods from a diverse range of R/Bioconductor packages into 'omics' data analysis workflows represents a significant challenge in terms of standardization, readability and reproducibility. Here, we present an R/Bioconductor package, named struct (Statistics in R using Class-based Templates), which defines a suite of class-based templates that allows users to develop and implement highly standardized and readable statistical analysis workflows. Struct integrates with the STATistics Ontology to ensure consistent reporting and maximizes semantic interoperability. We also present a toolbox, named structToolbox, which includes an extensive set of commonly used data analysis methods that have been implemented using struct. This toolbox can be used to build data-analysis workflows for metabolomics and other omics technologies. AVAILABILITY AND IMPLEMENTATION: struct and structToolbox are implemented in R, and are freely available from Bioconductor (http://bioconductor.org/packages/struct and http://bioconductor.org/packages/structToolbox), including documentation and vignettes. Source code is available and maintained at https://github.com/computational-metabolomics.
Gavin Rhys Lloyd, Andris Jankevics, Ralf J. M. Weber
Bioinform.1