Quantitative approaches to energy and glucose homeostasis: machine learning and modelling for precision understanding and prediction

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Title: Quantitative approaches to energy and glucose homeostasis: machine learning and modelling for precision understanding and prediction
Authors: McGrath, TM
Murphy, KG
Jones, NS
Item 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.
Issue Date: 24-Jan-2018
Date of Acceptance: 4-Jan-2018
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, which permits unrestricted use, provided the original author and source are credited.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: EP/N014529/1
Keywords: energy homeostasis
machine learning
mathematical biology
MD Multidisciplinary
General Science & Technology
Publication Status: Published online
Article Number: 20170736
Appears in Collections:Mathematics
Department of Medicine
Applied Mathematics and Mathematical Physics
Faculty of Medicine
Faculty of Natural Sciences

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