GREMA: modelling of emulated gene regulatory networks with confidence levels based on evolutionary intelligence to cope with the underdetermined problem
| Autor: | Ming-Ju Tsai, Shinn Ying Ho, Shinn Jang Ho, Jyun Rong Wang, Li Sun Shu, Wen Lin Huang |
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| Rok vydání: | 2020 |
| Předmět: |
Statistics and Probability
Optimization problem Underdetermined system Computer science Intelligence Gene regulatory network Inference computer.software_genre Biochemistry 03 medical and health sciences Genetic algorithm Gene Regulatory Networks Molecular Biology 030304 developmental biology 0303 health sciences 030302 biochemistry & molecular biology Ode Computational Biology Biological Evolution Confidence interval Computer Science Applications Computational Mathematics Computational Theory and Mathematics Ordinary differential equation Benchmark (computing) Data mining computer Algorithms |
| Zdroj: | Bioinformatics. 36:3833-3840 |
| ISSN: | 1460-2059 1367-4803 |
| DOI: | 10.1093/bioinformatics/btaa267 |
| Popis: | MotivationNon-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.ResultsThis 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.Availability and implementationAll of the datasets that were used and the GREMA-based tool are freely available at https://nctuiclab.github.io/GREMA.Supplementary informationSupplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
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