Emulating complex simulations by machine learning methods
Abstract Background The aim of the present paper is to construct an emulator of a complex biological system simulator using a machine learning approach. More specifically, the simulator is a patient-specific model that integrates metabolic, nutritional, and lifestyle data to predict the metabolic an...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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BMC
2021-11-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-021-04354-7 |
| _version_ | 1818837097982722048 |
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| author | Paola Stolfi Filippo Castiglione |
| author_facet | Paola Stolfi Filippo Castiglione |
| author_sort | Paola Stolfi |
| collection | DOAJ |
| description | Abstract Background The aim of the present paper is to construct an emulator of a complex biological system simulator using a machine learning approach. More specifically, the simulator is a patient-specific model that integrates metabolic, nutritional, and lifestyle data to predict the metabolic and inflammatory processes underlying the development of type-2 diabetes in absence of familiarity. Given the very high incidence of type-2 diabetes, the implementation of this predictive model on mobile devices could provide a useful instrument to assess the risk of the disease for aware individuals. The high computational cost of the developed model, being a mixture of agent-based and ordinary differential equations and providing a dynamic multivariate output, makes the simulator executable only on powerful workstations but not on mobile devices. Hence the need to implement an emulator with a reduced computational cost that can be executed on mobile devices to provide real-time self-monitoring. Results Similarly to our previous work, we propose an emulator based on a machine learning algorithm but here we consider a different approach which turn out to have better performances, indeed in terms of root mean square error we have an improvement of two order magnitude. We tested the proposed emulator on samples containing different number of simulated trajectories, and it turned out that the fitted trajectories are able to predict with high accuracy the entire dynamics of the simulator output variables. We apply the emulator to control the level of inflammation while leveraging on the nutritional input. Conclusion The proposed emulator can be implemented and executed on mobile health devices to perform quick-and-easy self-monitoring assessments. |
| first_indexed | 2024-12-19T03:17:06Z |
| format | Article |
| id | doaj.art-a6a5641db45c41bfb580a4adbb2f8c7d |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-19T03:17:06Z |
| publishDate | 2021-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-a6a5641db45c41bfb580a4adbb2f8c7d2022-12-21T20:37:52ZengBMCBMC Bioinformatics1471-21052021-11-0122S1411410.1186/s12859-021-04354-7Emulating complex simulations by machine learning methodsPaola Stolfi0Filippo Castiglione1Institute for Applied Computing, National Research Council of ItalyInstitute for Applied Computing, National Research Council of ItalyAbstract Background The aim of the present paper is to construct an emulator of a complex biological system simulator using a machine learning approach. More specifically, the simulator is a patient-specific model that integrates metabolic, nutritional, and lifestyle data to predict the metabolic and inflammatory processes underlying the development of type-2 diabetes in absence of familiarity. Given the very high incidence of type-2 diabetes, the implementation of this predictive model on mobile devices could provide a useful instrument to assess the risk of the disease for aware individuals. The high computational cost of the developed model, being a mixture of agent-based and ordinary differential equations and providing a dynamic multivariate output, makes the simulator executable only on powerful workstations but not on mobile devices. Hence the need to implement an emulator with a reduced computational cost that can be executed on mobile devices to provide real-time self-monitoring. Results Similarly to our previous work, we propose an emulator based on a machine learning algorithm but here we consider a different approach which turn out to have better performances, indeed in terms of root mean square error we have an improvement of two order magnitude. We tested the proposed emulator on samples containing different number of simulated trajectories, and it turned out that the fitted trajectories are able to predict with high accuracy the entire dynamics of the simulator output variables. We apply the emulator to control the level of inflammation while leveraging on the nutritional input. Conclusion The proposed emulator can be implemented and executed on mobile health devices to perform quick-and-easy self-monitoring assessments.https://doi.org/10.1186/s12859-021-04354-7Type-2 diabetesEmulationComputational modellingRisk predictionSelf-assessment |
| spellingShingle | Paola Stolfi Filippo Castiglione Emulating complex simulations by machine learning methods BMC Bioinformatics Type-2 diabetes Emulation Computational modelling Risk prediction Self-assessment |
| title | Emulating complex simulations by machine learning methods |
| title_full | Emulating complex simulations by machine learning methods |
| title_fullStr | Emulating complex simulations by machine learning methods |
| title_full_unstemmed | Emulating complex simulations by machine learning methods |
| title_short | Emulating complex simulations by machine learning methods |
| title_sort | emulating complex simulations by machine learning methods |
| topic | Type-2 diabetes Emulation Computational modelling Risk prediction Self-assessment |
| url | https://doi.org/10.1186/s12859-021-04354-7 |
| work_keys_str_mv | AT paolastolfi emulatingcomplexsimulationsbymachinelearningmethods AT filippocastiglione emulatingcomplexsimulationsbymachinelearningmethods |