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

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

SUBMITTER: Coggan H 

PROVIDER: S-EPMC9510846 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature