Reference : Gene regulatory network inference from sparsely sampled noisy data
Scientific journals : Article
Life sciences : Genetics & genetic processes
Computational Sciences
http://hdl.handle.net/10993/36442
Gene regulatory network inference from sparsely sampled noisy data
English
Aalto, Atte mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Viitasaari, Lauri []
Ilmonen, Pauliina []
Mombaerts, Laurent mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Goncalves, Jorge mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
13-Jul-2020
Nature Communications
Nature Publishing Group
11
3493
Yes (verified by ORBilu)
2041-1723
London
United Kingdom
[en] Gene regulatory networks ; Gaussian processes ; Reverse engineering ; Systems biology ; Stochastic differential equations
[en] The complexity of biological systems is encoded in gene regulatory networks. Unravelling this intricate web is a fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases. The major obstacle in inferring gene regulatory networks is the lack of data. While time series data are nowadays widely available, they are typically noisy, with low sampling frequency and overall small number of samples. This paper develops a method called BINGO to specifically deal with these issues. Benchmarked with both real and simulated time-series data covering many different gene regulatory networks, BINGO clearly and consistently outperforms state-of-the-art methods. The novelty of BINGO lies in a nonparametric approach featuring statistical sampling of continuous gene expression profiles. BINGO’s superior performance and ease of use, even by non-specialists, make gene regulatory network inference available to any researcher, helping to decipher the complex mechanisms of life.
Researchers ; Professionals
http://hdl.handle.net/10993/36442
10.1038/s41467-020-17217-1
https://www.nature.com/articles/s41467-020-17217-1
FnR ; FNR8888477 > Jorge Gon�alves > CropClock > Increasing crops biomass by uncovering the circadian clock network using dynamical models > 01/01/2015 > 30/06/2018 > 2014

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