Learning Equations from Biological Data with Limited Time Samples
| Autor: | Lee Curtin, John T. Nardini, Andrea Hawkins-Daarud, Erica M. Rutter, Bethan Morris, John H. Lagergren, Kevin B. Flores, Kristin R. Swanson |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Dynamical systems theory General Mathematics Immunology Machine learning computer.software_genre Models Biological General Biochemistry Genetics and Molecular Biology Article 03 medical and health sciences 0302 clinical medicine Humans Learning General Environmental Science Sparse matrix Pharmacology Biological data Partial differential equation business.industry Estimation theory General Neuroscience Model selection Sampling (statistics) Computational Biology Mathematical Concepts System dynamics 030104 developmental biology Computational Theory and Mathematics Nonlinear Dynamics 030220 oncology & carcinogenesis Artificial intelligence General Agricultural and Biological Sciences business Glioblastoma computer |
| Zdroj: | Bull Math Biol |
| ISSN: | 1522-9602 |
| Popis: | Equation learning methods present a promising tool to aid scientists in the modeling process for biological data. Previous equation learning studies have demonstrated that these methods can infer models from rich datasets; however, the performance of these methods in the presence of common challenges from biological data has not been thoroughly explored. We present an equation learning methodology comprised of data denoising, equation learning, model selection and post-processing steps that infers a dynamical systems model from noisy spatiotemporal data. The performance of this methodology is thoroughly investigated in the face of several common challenges presented by biological data, namely, sparse data sampling, large noise levels, and heterogeneity between datasets. We find that this methodology can accurately infer the correct underlying equation and predict unobserved system dynamics from a small number of time samples when the data are sampled over a time interval exhibiting both linear and nonlinear dynamics. Our findings suggest that equation learning methods can be used for model discovery and selection in many areas of biology when an informative dataset is used. We focus on glioblastoma multiforme modeling as a case study in this work to highlight how these results are informative for data-driven modeling-based tumor invasion predictions. |
| Databáze: | OpenAIRE |
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