DIVIS: a semantic DIstance to improve the VISualisation of heterogeneous phenotypic datasets

DIVIS: a semantic DIstance to improve the VISualisation of heterogeneous phenotypic datasets

Abstract Background Thanks to the wider spread of high-throughput experimental techniques, biologists are accumulating large amounts of datasets which often mix quantitative and qualitative variables and are not always complete, in particular when they regard phenotypic traits. In order to get a fir...

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Bibliographic Details
Main Authors: Rayan Eid, Claudine Landès, Alix Pernet, Emmanuel Benoît, Pierre Santagostini, Angelina El Ghaziri, Julie Bourbeillon
Format: Article
Language:English
Published: BMC 2022-04-01
Series:BioData Mining
Subjects:
Mixed datasets
Heterogeneous datasets
Phenotypic traits
Multivariate analysis
Ontologies
Semantic distance
Online Access:https://doi.org/10.1186/s13040-022-00293-y

Internet

https://doi.org/10.1186/s13040-022-00293-y

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