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Gianmarco Silluzio

dblp:370/4778 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 1 · 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 · 100%

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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › gene expression analysis
differential expression analysis		 0.712023
DEGGs: an R package with shiny app for the identification of differentially expressed gene-gene interactions in high-throughput sequencing data · Bioinform. 2023
Bioinformatics and computational biology › statistical genetics
gene-gene interaction		 0.712023
DEGGs: an R package with shiny app for the identification of differentially expressed gene-gene interactions in high-throughput sequencing data · Bioinform. 2023
Bioinformatics and computational biology › gene regulation
gene regulatory network		 0.212023
DEGGs: an R package with shiny app for the identification of differentially expressed gene-gene interactions in high-throughput sequencing data · Bioinform. 2023

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

robust linear regression · 0.7
YearPublicationVenuePosition
2023 DEGGs: an R package with shiny app for the identification of differentially expressed gene-gene interactions in high-throughput sequencing data
abstract
SUMMARY: The discovery of differential gene-gene correlations across phenotypical groups can help identify the activation/deactivation of critical biological processes underlying specific conditions. The presented R package, provided with a count and design matrix, extract networks of group-specific interactions that can be interactively explored through a shiny user-friendly interface. For each gene-gene link, differential statistical significance is provided through robust linear regression with an interaction term. AVAILABILITY AND IMPLEMENTATION: DEGGs is implemented in R and available on GitHub at https://github.com/elisabettasciacca/DEGGs. The package is also under submission on Bioconductor.
Elisabetta C. Sciacca, Salvatore Alaimo, Gianmarco Silluzio, Alfredo Ferro, Vito Latora, Costantino Pitzalis, Alfredo Pulvirenti, Myles J. Lewis
 Bioinform.3