Can ODE gene regulatory models neglect time lag or measurement scaling?
Author(s) -
Jie Hu,
Huihui Qin,
Xiaodan Fan
Publication year - 2020
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btaa268
Subject(s) - ode , lag , computer science , ordinary differential equation , gene regulatory network , linear model , data mining , econometrics , mathematics , biology , differential equation , gene , machine learning , genetics , gene expression , computer network , mathematical analysis
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.
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