rScudo: an R package for classification of molecular profiles using rank-based signatures.


Journal

Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944

Informations de publication

Date de publication:
01 07 2020
Historique:
received: 06 05 2019
revised: 13 01 2020
accepted: 05 05 2020
pubmed: 14 5 2020
medline: 29 12 2020
entrez: 14 5 2020
Statut: ppublish

Résumé

The classification of biological samples by means of their respective molecular profiles is a topic of great interest for its potential diagnostic, prognostic and investigational applications. rScudo is an R package for the classification of molecular profiles based on a radically new approach consisting in the analysis of the similarity of rank-based sample-specific signatures. The validity of rScudo unconventional approach has been validated through direct comparison with current methods in the international SBV IMPROVER Diagnostic Signature Challenge. Due to its novelty, there is ample room for conceptual improvements and for exploring additional applications. The rScudo package has been specifically designed to facilitate experimenting with the rank-based signature approach, to test its application to different types of molecular profiles and to simplify direct comparison with existing methods. The package is available as part of the Bioconductor suite at https://bioconductor.org/packages/rScudo.

Identifiants

pubmed: 32399554
pii: 5836499
doi: 10.1093/bioinformatics/btaa296
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

4095-4096

Informations de copyright

© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Auteurs

Matteo Ciciani (M)

CIBIO--Centre for Integrative Biology.

Thomas Cantore (T)

CIBIO--Centre for Integrative Biology.

Mario Lauria (M)

Department of Mathematics, University of Trento, 38123 Povo, Trentino, Italy.
The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), 38068 Rovereto, Trentino, Italy.

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Classifications MeSH