HiDi: an efficient reverse engineering schema for large-scale dynamic regulatory network reconstruction using adaptive differentiation

Handle URI:
http://hdl.handle.net/10754/626582
Title:
HiDi: an efficient reverse engineering schema for large-scale dynamic regulatory network reconstruction using adaptive differentiation
Authors:
Deng, Yue; Zenil, Hector ( 0000-0003-0634-4384 ) ; Tegner, Jesper ( 0000-0002-9568-5588 ) ; Kiani, Narsis A.
Abstract:
Motivation: The use of differential equations (ODE) is one of the most promising approaches to network inference. The success of ODE-based approaches has, however, been limited, due to the difficulty in estimating parameters and by their lack of scalability. Here, we introduce a novel method and pipeline to reverse engineer gene regulatory networks from gene expression of time series and perturbation data based upon an improvement on the calculation scheme of the derivatives and a pre-filtration step to reduce the number of possible links. The method introduces a linear differential equation model with adaptive numerical differentiation that is scalable to extremely large regulatory networks. Results: We demonstrate the ability of this method to outperform current state-of-the-art methods applied to experimental and synthetic data using test data from the DREAM4 and DREAM5 challenges. Our method displays greater accuracy and scalability. We benchmark the performance of the pipeline with respect to dataset size and levels of noise. We show that the computation time is linear over various network sizes.
KAUST Department:
Biological and Environmental Sciences and Engineering (BESE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Deng Y, Zenil H, Tegnér J, Kiani NA (2017) HiDi: an efficient reverse engineering schema for large-scale dynamic regulatory network reconstruction using adaptive differentiation. Bioinformatics 33: 3964–3972. Available: http://dx.doi.org/10.1093/bioinformatics/btx501.
Publisher:
Oxford University Press (OUP)
Journal:
Bioinformatics
Issue Date:
5-Aug-2017
DOI:
10.1093/bioinformatics/btx501
Type:
Article
ISSN:
1367-4803; 1460-2059
Sponsors:
Vinnova VINNMER fellowship, Stratneuro (to N.A.K.); Swedish Research Council - Vetenskapsra°det (to H.Z.). The founders played no role in the design of the study, in data collection and analysis, in the decision to publish, or in the preparation of the manuscript.
Additional Links:
https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btx501/4076057/HiDi-an-efficient-reverse-engineering-schema-for
Appears in Collections:
Articles; Biological and Environmental Sciences and Engineering (BESE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorDeng, Yueen
dc.contributor.authorZenil, Hectoren
dc.contributor.authorTegner, Jesperen
dc.contributor.authorKiani, Narsis A.en
dc.date.accessioned2018-01-01T12:19:01Z-
dc.date.available2018-01-01T12:19:01Z-
dc.date.issued2017-08-05en
dc.identifier.citationDeng Y, Zenil H, Tegnér J, Kiani NA (2017) HiDi: an efficient reverse engineering schema for large-scale dynamic regulatory network reconstruction using adaptive differentiation. Bioinformatics 33: 3964–3972. Available: http://dx.doi.org/10.1093/bioinformatics/btx501.en
dc.identifier.issn1367-4803en
dc.identifier.issn1460-2059en
dc.identifier.doi10.1093/bioinformatics/btx501en
dc.identifier.urihttp://hdl.handle.net/10754/626582-
dc.description.abstractMotivation: The use of differential equations (ODE) is one of the most promising approaches to network inference. The success of ODE-based approaches has, however, been limited, due to the difficulty in estimating parameters and by their lack of scalability. Here, we introduce a novel method and pipeline to reverse engineer gene regulatory networks from gene expression of time series and perturbation data based upon an improvement on the calculation scheme of the derivatives and a pre-filtration step to reduce the number of possible links. The method introduces a linear differential equation model with adaptive numerical differentiation that is scalable to extremely large regulatory networks. Results: We demonstrate the ability of this method to outperform current state-of-the-art methods applied to experimental and synthetic data using test data from the DREAM4 and DREAM5 challenges. Our method displays greater accuracy and scalability. We benchmark the performance of the pipeline with respect to dataset size and levels of noise. We show that the computation time is linear over various network sizes.en
dc.description.sponsorshipVinnova VINNMER fellowship, Stratneuro (to N.A.K.); Swedish Research Council - Vetenskapsra°det (to H.Z.). The founders played no role in the design of the study, in data collection and analysis, in the decision to publish, or in the preparation of the manuscript.en
dc.publisherOxford University Press (OUP)en
dc.relation.urlhttps://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btx501/4076057/HiDi-an-efficient-reverse-engineering-schema-foren
dc.titleHiDi: an efficient reverse engineering schema for large-scale dynamic regulatory network reconstruction using adaptive differentiationen
dc.typeArticleen
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalBioinformaticsen
dc.contributor.institutionUnit of Computational Medicine, Center for Molecular Medicine, Department of Medicine, Solna and Science for Life Laboratory (SciLifeLab), Karolinska Institute, Stockholm, Swedenen
dc.contributor.institutionAlgorithmic Dynamics Lab, Center for Molecular Medicine, Department of Medicine, Solna and Science for Life Laboratory (SciLifeLab), Karolinska Institute, Stockholm, Swedenen
kaust.authorTegner, Jesperen
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