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Quantitative approaches to energy and glucose homeostasis: machine learning and modelling for precision understanding and prediction
File(s)
20170736.full.pdf (1.1 MB)
Published version
Author(s)
McGrath, TM
Murphy, Kevin G
Jones, Nick S
Type
Journal Article
Abstract
Obesity is a major global public health problem. Understanding how energy homeostasis is regulated, and can become dysregulated, is crucial for developing new treatments for obesity. Detailed recording of individual behaviour and new imaging modalities offer the prospect of medically relevant models of energy homeostasis that are both understandable and individually predictive. The profusion of data from these sources has led to an interest in applying machine learning techniques to gain insight from these large, relatively unstructured datasets. We review both physiological models and machine learning results across a diverse range of applications in energy homeostasis, and highlight how modelling and machine learning can work together to improve predictive ability. We collect quantitative details in a comprehensive mathematical supplement. We also discuss the prospects of forecasting homeostatic behaviour and stress the importance of characterizing stochasticity within and between individuals in order to provide practical, tailored forecasts and guidance to combat the spread of obesity.
Date Issued
2018-01-24
Date Acceptance
2018-01-04
Citation
Journal of the Royal Society Interface, 2018, 15
URI
DOI
ISSN
1742-5662
Publisher
Royal Society, The
Journal / Book Title
Journal of the Royal Society Interface
Volume
15
Copyright Statement
© 2018 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution
License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original
author and source are credited.
License URL
Sponsor
Engineering & Physical Science Research Council (EPSRC)
BBSRC DTP
Grant Number
EP/N014529/1
BB/J014575/1
Subjects
energy homeostasis
glucostasis
machine learning
mathematical biology
MD Multidisciplinary
General Science & Technology
Publication Status
Published online
Article Number
20170736