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Nino Spataro

dblp:258/2626 · DBLP profile ↗
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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 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › statistical genetics › genotype-phenotype association
disease association
0.112020
Genome-phenome explorer (GePhEx): a tool for the visualization and interpretation of phenotypic relationships supported by genetic evidence · Bioinform. 2020
Bioinformatics and computational biology › population genetics
linkage disequilibrium
0.112020
Genome-phenome explorer (GePhEx): a tool for the visualization and interpretation of phenotypic relationships supported by genetic evidence · Bioinform. 2020

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

pathway analysis · 0.4linkage disequilibrium analysis · 0.4
YearPublicationVenuePosition
2020 Genome-phenome explorer (GePhEx): a tool for the visualization and interpretation of phenotypic relationships supported by genetic evidence
abstract
MOTIVATION: Association studies based on SNP arrays and Next Generation Sequencing technologies have enabled the discovery of thousands of genetic loci related to human diseases. Nevertheless, their biological interpretation is still elusive, and their medical applications limited. Recently, various tools have been developed to help bridging the gap between genomes and phenomes. To our knowledge, however none of these tools allows users to retrieve the phenotype-wide list of genetic variants that may be linked to a given disease or to visually explore the joint genetic architecture of different pathologies. RESULTS: We present the Genome-Phenome Explorer (GePhEx), a web-tool easing the visual exploration of phenotypic relationships supported by genetic evidences. GePhEx is primarily based on the thorough analysis of linkage disequilibrium between disease-associated variants and also considers relationships based on genes, pathways or drug-targets, leveraging on publicly available variant-disease associations to detect potential relationships between diseases. We demonstrate that GePhEx does retrieve well-known relationships as well as novel ones, and that, thus, it might help shedding light on the patho-physiological mechanisms underlying complex diseases. To this end, we investigate the potential relationship between schizophrenia and lung cancer, first detected using GePhEx and provide further evidence supporting a functional link between them. AVAILABILITY AND IMPLEMENTATION: GePhEx is available at: https://gephex.ega-archive.org/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Xavier Farré, Nino Spataro, Frédéric Haziza, Jordi Rambla De Argila, Arcadi Navarro
Bioinform.2