Demo Data from SIGIR 2026 / reviewers in the wild / expert
Gilles Renand
dblp:276/0637
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1
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 · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › data integration
heterogeneous data integration |
0.4 | 1 | 2020 | Link-HD: a versatile framework to explore and integrate heterogeneous microbial communities · Bioinform. 2020 |
Bioinformatics and computational biology › computational microbiology
microbiome analysis |
0.4 | 1 | 2020 | Link-HD: a versatile framework to explore and integrate heterogeneous microbial communities · Bioinform. 2020 |
Bioinformatics and computational biology
metagenomics |
0.1 | 1 | 2020 | Link-HD: a versatile framework to explore and integrate heterogeneous microbial communities · Bioinform. 2020 |
Methods — techniques the papers use, named apart from their topics
variable selection · 0.4taxon-set enrichment analysis · 0.4multi-table ordination · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Link-HD: a versatile framework to explore and integrate heterogeneous microbial communitiesabstractMOTIVATION: We present Link-HD, an approach to integrate multiple datasets. Link-HD is a generalization of 'Structuration des Tableaux A Trois Indices de la Statistique-Analyse Conjointe de Tableaux', a family of methods designed to integrate information from heterogeneous data. Here, we extend the classical approach to deal with broader datasets (e.g. compositional data), methods for variable selection and taxon-set enrichment analysis. RESULTS: The methodology is demonstrated by integrating rumen microbial communities from cows for which methane yield (CH4y) was individually measured. Our approach reproduces the significant link between rumen microbiota structure and CH4 emission. When analyzing the TARA's ocean data, Link-HD replicates published results, highlighting the relevance of temperature with members of phyla Proteobacteria on the structure and functionality of this ecosystem. AVAILABILITY AND IMPLEMENTATION: The source code, examples and a complete manual are freely available in GitHub https://github.com/lauzingaretti/LinkHD and in Bioconductor https://bioconductor.org/packages/release/bioc/html/LinkHD.html. Laura M. Zingaretti, Gilles Renand, Diego P. Morgavi, Yuliaxis Ramayo-Caldas |
Bioinform. | 2 |