In this section
@article{McGrath:2018:10.1098/rsif.2017.0736,
author = {McGrath, TM and Murphy, KG and Jones, NS},
doi = {10.1098/rsif.2017.0736},
journal = {Journal of the Royal Society Interface},
title = {Quantitative approaches to energy and glucose homeostasis: machine learning and modelling for precision understanding and prediction},
url = {http://dx.doi.org/10.1098/rsif.2017.0736},
volume = {15},
year = {2018}
}
TY - JOUR
AB - 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.
AU - McGrath,TM
AU - Murphy,KG
AU - Jones,NS
DO - 10.1098/rsif.2017.0736
PY - 2018///
SN - 1742-5662
TI - Quantitative approaches to energy and glucose homeostasis: machine learning and modelling for precision understanding and prediction
T2 - Journal of the Royal Society Interface
UR - http://dx.doi.org/10.1098/rsif.2017.0736
UR - http://hdl.handle.net/10044/1/55833
VL - 15
ER -