Can ODE gene regulatory models neglect time lag or measurement scaling?
Publication in refereed journal


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AbstractMotivation
Many ordinary differential equation (ODE) models have been introduced to replace linear regression models for inferring gene regulatory relationships from time-course gene expression data. But, since the observed data are usually not direct measurements of the gene products or there is an unknown time lag in gene regulation, it is problematic to directly apply traditional ODE models or linear regression models.

Results
We introduce a lagged ODE model to infer lagged gene regulatory relationships from time-course measurements, which are modeled as linear transformation of the gene products. A time-course microarray dataset from a yeast cell-cycle study is used for simulation assessment of the methods and real data analysis. The results show that our method, by considering both time lag and measurement scaling, performs much better than other linear and ODE models. It indicates the necessity of explicitly modeling the time lag and measurement scaling in ODE gene regulatory models.

Availability and implementation
R code is available at https://www.sta.cuhk.edu.hk/xfan/share/lagODE.zip.
Acceptance Date16/04/2020
All Author(s) ListHu J., Qin H., Fan X.
Journal nameBioinformatics
Year2020
Month7
Volume Number36
Issue Number13
PublisherNLM (Medline)
Place of PublicationOxford, England
Pages4058 - 4064
ISSN1367-4803
eISSN1460-2059
LanguagesEnglish-United Kingdom

Last updated on 2020-15-10 at 00:32