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A novel interpretable machine learning algorithm to identify optimal parameter space for cancer growth

Coggan, H; Andres Terre, H; Liò, P; (2022) A novel interpretable machine learning algorithm to identify optimal parameter space for cancer growth. Frontiers in Big Data , 5 , Article 941451. 10.3389/fdata.2022.941451. Green open access

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Abstract

Recent years have seen an increase in the application of machine learning to the analysis of physical and biological systems, including cancer progression. A fundamental downside to these tools is that their complexity and nonlinearity makes it almost impossible to establish a deterministic, a priori relationship between their input and output, and thus their predictions are not wholly accountable. We begin with a series of proofs establishing that this holds even for the simplest possible model of a neural network; the effects of specific loss functions are explored more fully in Appendices. We return to first principles and consider how to construct a physics-inspired model of tumor growth without resorting to stochastic gradient descent or artificial nonlinearities. We derive an algorithm which explores the space of possible parameters in a model of tumor growth and identifies candidate equations much faster than a simulated annealing approach. We test this algorithm on synthetic tumor-growth trajectories and show that it can efficiently and reliably narrow down the area of parameter space where the correct values are located. This approach has the potential to greatly improve the speed and reliability with which patient-specific models of cancer growth can be identified in a clinical setting.

Type: Article
Title: A novel interpretable machine learning algorithm to identify optimal parameter space for cancer growth
Open access status: An open access version is available from UCL Discovery
DOI: 10.3389/fdata.2022.941451
Publisher version: https://doi.org/10.3389/fdata.2022.941451
Language: English
Additional information: © 2022 Coggan, Andres Terre and Liò. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/).
Keywords: cancer, neural networks, white-box machine learning, interpretability, parameter optimization
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Mathematics
URI: https://discovery.ucl.ac.uk/id/eprint/10160178
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