Tradict enables accurate prediction of eukaryotic transcriptional states from 100 marker genes

Surojit Biswas; Konstantin Kerner; Paulo José Pereira Lima Teixeira; Jeffery L. Dangl; Vladimir Jojic; Philip A. Wigge

doi:10.1038/ncomms15309

Nature Communications (May 2017)

Tradict enables accurate prediction of eukaryotic transcriptional states from 100 marker genes

Surojit Biswas,
Konstantin Kerner,
Paulo José Pereira Lima Teixeira,
Jeffery L. Dangl,
Vladimir Jojic,
Philip A. Wigge

Affiliations

Surojit Biswas: Department of Biomedical Informatics, Harvard Medical School
Konstantin Kerner: Botanical Institute, Biocenter, University of Cologne
Paulo José Pereira Lima Teixeira: Howard Hughes Medical Institute, University of North Carolina at Chapel Hill
Jeffery L. Dangl: Howard Hughes Medical Institute, University of North Carolina at Chapel Hill
Vladimir Jojic: Department of Computer Science, University of North Carolina at Chapel Hill
Philip A. Wigge: Sainsbury Laboratory, University of Cambridge

DOI: https://doi.org/10.1038/ncomms15309
Journal volume & issue: Vol. 8, no. 1
pp. 1 – 10

Abstract

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Global patterns of gene transcription can be represented with reduced dimensionality. Here, the authors devise a method called Tradict that learns and uses 100 marker genes to predict transcriptome-wide pathway expression levels and patterns that reflect cell activity and state.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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