Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review
Abstract
:1. Introduction
2. Machine Learning Models
3. Mechanistic Models
4. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
RCC | Renal Cell Carcinoma |
GSEA | Gene Set Enrichment Analysis |
TCGA | The Cancer Genome Atlas |
VHL | Von Hippel–Lindau |
HIF | Hypoxia-Inducible Factor |
ODE | Ordinary Differential Equation |
GFP | Green Fluorescent Protein |
MRI | Magnetic Resonance Imaging |
PDE | Partial Differential Equation |
UMAP | Uniform Manifold Approximation and Projection |
CNN | Convolutional Neural Network |
RNN | Recurrent Neural Network |
RSF-VH | Random Survival Forests-Variable Hunting |
FDE | Fractional Differential Equation |
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Sofia, D.; Zhou, Q.; Shahriyari, L. Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review. Bioengineering 2023, 10, 1320. https://doi.org/10.3390/bioengineering10111320
Sofia D, Zhou Q, Shahriyari L. Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review. Bioengineering. 2023; 10(11):1320. https://doi.org/10.3390/bioengineering10111320
Chicago/Turabian StyleSofia, Dilruba, Qilu Zhou, and Leili Shahriyari. 2023. "Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review" Bioengineering 10, no. 11: 1320. https://doi.org/10.3390/bioengineering10111320
APA StyleSofia, D., Zhou, Q., & Shahriyari, L. (2023). Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review. Bioengineering, 10(11), 1320. https://doi.org/10.3390/bioengineering10111320