<p>GOexpress: A R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data</p>

Kevin Rue-Albrecht; Paul A. McGettigan; Belinda  Hernández; Nicolas, C. Nalpas; David A. Magee; Andrew C.  Parnell; Stephen V. Gordon; David E. MacHugh

doi:10.7490/f1000research.1000221.1

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Rue-Albrecht K, McGettigan PA, Hernández B et al. GOexpress: A R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data [version 1; not peer reviewed]. F1000Research 2015, 4(ISCB Comm J):247 (poster) (https://doi.org/10.7490/f1000research.1000221.1)

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GOexpress: A R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data

Kevin Rue-Albrecht¹, Paul A. McGettigan, Belinda Hernández, Nicolas, C. Nalpas, David A. Magee, Andrew C. Parnell, Stephen V. Gordon, David E. MacHugh

Published 14 Jul 2015 (https://doi.org/10.7490/f1000research.1000221.1)

Author Affiliations

¹ University College Dublin, Ireland

Copyright © 2015 Rue-Albrecht K et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Published 14 Jul 2015

GOexpress: A R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data
[version 1; not peer reviewed]

Kevin Rue-Albrecht¹, Paul A. McGettigan, Belinda Hernández, Nicolas, C. Nalpas, David A. Magee, Andrew C. Parnell, Stephen V. Gordon, David E. MacHugh

Author Affiliations

¹ University College Dublin, Ireland

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Bioinformatics Open Source Conference (BOSC)

Quantitative Genomics (QG)

Abstract

Competing Interests

No competing interests were disclosed

Keywords

Gene expression, gene ontology, supervised learning, classification, microarray, RNA-sequencing, functional genomics

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