Journal
BMC BIOINFORMATICS
Volume 21, Issue -, Pages -Publisher
BMC
DOI: 10.1186/s12859-020-03763-4
Keywords
Machine learning; Random forest; Emulator; T2D; Computational modeling; Synthetic data
Categories
Funding
- European Commission under the 7th Framework Programme (MISSION-T2D project) [600803]
- Horizon 2020 research and innovation programme (iPC project) [826121]
- H2020 Societal Challenges Programme [826121] Funding Source: H2020 Societal Challenges Programme
Ask authors/readers for more resources
BackgroundThe aim of a recent research project was the investigation of the mechanisms involved in the onset of type 2 diabetes in the absence of familiarity. This has led to the development of a computational model that recapitulates the aetiology of the disease and simulates the immunological and metabolic alterations linked to type-2 diabetes subjected to clinical, physiological, and behavioural features of prototypical human individuals.ResultsWe analysed the time course of 46,170 virtual subjects, experiencing different lifestyle conditions. We then set up a statistical model able to recapitulate the simulated outcomes.ConclusionsThe resulting machine learning model adequately predicts the synthetic dataset and can, therefore, be used as a computationally-cheaper version of the detailed mathematical model, ready to be implemented on mobile devices to allow self-assessment by informed and aware individuals. The computational model used to generate the dataset of this work is available as a web-service at the following address: http://kraken.iac.rm.cnr.it/T2DM.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available