GREMA: modelling of emulated gene regulatory networks with confidence levels based on evolutionary intelligence to cope with the underdetermined problem | Zendy

Ming-Ju Tsai | Zendy; Jyun-Rong Wang | Zendy; Shinn-Jang Ho | Zendy; Li-Sun Shu | Zendy; Wen-Lin Huang | Zendy; ShinnYing Ho | Zendy

Open Access

GREMA: modelling of emulated gene regulatory networks with confidence levels based on evolutionary intelligence to cope with the underdetermined problem

Author(s) -

Ming-Ju Tsai,

Jyun-Rong Wang,

Shinn-Jang Ho,

Li-Sun Shu,

Wen-Lin Huang,

ShinnYing Ho

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/btaa267

Subject(s) - underdetermined system , benchmark (surveying) , ode , computer science , inference , gene regulatory network , evolutionary algorithm , confidence interval , genetic algorithm , artificial intelligence , machine learning , data mining , algorithm , mathematical optimization , statistics , mathematics , biology , gene , genetics , gene expression , geodesy , geography

Non-linear ordinary differential equation (ODE) models that contain numerous parameters are suitable for inferring an emulated gene regulatory network (eGRN). However, the number of experimental measurements is usually far smaller than the number of parameters of the eGRN model that leads to an underdetermined problem. There is no unique solution to the inference problem for an eGRN using insufficient measurements.

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