Gene regulatory network inference from sparsely sampled noisy data

Atte Aalto; Lauri Viitasaari; Pauliina Ilmonen; Laurent Mombaerts; Jorge Gonçalves

doi:10.1038/s41467-020-17217-1

Nature Communications (Jul 2020)

Gene regulatory network inference from sparsely sampled noisy data

Atte Aalto,
Lauri Viitasaari,
Pauliina Ilmonen,
Laurent Mombaerts,
Jorge Gonçalves

Affiliations

Atte Aalto: Luxembourg Centre for Systems Biomedicine, University of Luxembourg; 6 avenue du Swing
Lauri Viitasaari: Department of Mathematics and Statistics, University of Helsinki; P.O. Box 68, Gustaf Hällströmin katu 2b
Pauliina Ilmonen: Department of Mathematics and Systems Analysis, Aalto University School of Science; P.O. Box 11100
Laurent Mombaerts: Luxembourg Centre for Systems Biomedicine, University of Luxembourg; 6 avenue du Swing
Jorge Gonçalves: Luxembourg Centre for Systems Biomedicine, University of Luxembourg; 6 avenue du Swing

DOI: https://doi.org/10.1038/s41467-020-17217-1
Journal volume & issue: Vol. 11, no. 1
pp. 1 – 9

Abstract

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Gene regulatory network inference is a topical problem in systems biology. Here, the authors presents BINGO, a powerful method for network inference from time series data.

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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