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Samuel Heron

dblp:148/4726 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none

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 1 heaviest of 1, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › functional genomics › functional enrichment analysis
gene ontology analysis
0.212014
GOssTo: a stand-alone application and a web tool for calculating semantic similarities on the Gene Ontology · Bioinform. 2014

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

term-based similarity · 0.2random walk · 0.2graph-based similarity · 0.2
YearPublicationVenuePosition
2014 GOssTo: a stand-alone application and a web tool for calculating semantic similarities on the Gene Ontology
abstract
SUMMARY: We present GOssTo, the Gene Ontology semantic similarity Tool, a user-friendly software system for calculating semantic similarities between gene products according to the Gene Ontology. GOssTo is bundled with six semantic similarity measures, including both term- and graph-based measures, and has extension capabilities to allow the user to add new similarities. Importantly, for any measure, GOssTo can also calculate the Random Walk Contribution that has been shown to greatly improve the accuracy of similarity measures. GOssTo is very fast, easy to use, and it allows the calculation of similarities on a genomic scale in a few minutes on a regular desktop machine. CONTACT: [email protected] AVAILABILITY: GOssTo is available both as a stand-alone application running on GNU/Linux, Windows and MacOS from www.paccanarolab.org/gossto and as a web application from www.paccanarolab.org/gosstoweb. The stand-alone application features a simple and concise command line interface for easy integration into high-throughput data processing pipelines.
Horacio Caniza, Alfonso E. Romero, Samuel Heron, Haixuan Yang, Alessandra Devoto, Marco Frasca 0001, Marco Mesiti, Giorgio Valentini, Alberto Paccanaro
Bioinform.3