標題: GREMA: modelling of emulated gene regulatory networks with confidence levels based on evolutionary intelligence to cope with the underdetermined problem
作者: Tsai, Ming-Ju
Wang, Jyun-Rong
Hoe, Shinn-Jang
Shu, Li-Sun
Huang, Wen-Lin
Ho, Shinn-Ying
交大名義發表
生物科技學系
生物資訊及系統生物研究所
National Chiao Tung University
Department of Biological Science and Technology
Institude of Bioinformatics and Systems Biology
公開日期: 15-Jun-2020
摘要: Motivation: 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. Results: This work proposes an evolutionary modelling algorithm (EMA) that is based on evolutionary intelligence to cope with the underdetermined problem. EMA uses an intelligent genetic algorithm to solve the large-scale parameter optimization problem. An EMA-based method, GREMA, infers a novel type of gene regulatory network with confidence levels for every inferred regulation. The higher the confidence level is, the more accurate the inferred regulation is. GREMA gradually determines the regulations of an eGRN with confidence levels in descending order using either an S-system or a Hill function-based ODE model. The experimental results showed that the regulations with high-confidence levels are more accurate and robust than regulations with low-confidence levels. Evolutionary intelligence enhanced the mean accuracy of GREMA by 19.2% when using the S-system model with benchmark datasets. An increase in the number of experimental measurements may increase the mean confidence level of the inferred regulations. GREMA performed well compared with existing methods that have been previously applied to the same S-system, DREAM4 challenge and SOS DNA repair benchmark datasets.
URI: http://dx.doi.org/10.1093/bioinformatics/btaa267
http://hdl.handle.net/11536/155076
ISSN: 1367-4803
DOI: 10.1093/bioinformatics/btaa267
期刊: BIOINFORMATICS
Volume: 36
Issue: 12
起始頁: 3833
結束頁: 3840
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