Machine Learning Identifies Robust Matrisome Markers and Regulatory Mechanisms in Cancer
Abstract
:1. Introduction
2. Results
3. Discussion
4. Materials and Methods
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
Abbreviations
WGCNA | Weighted gene coexpression analysis |
LASSO | Least absolute shrinkage and selection operator |
miRNA | Micro-RNAs |
sPCR | Sparse principal component regression |
RFR | Random forest regression |
TCGA | The Cancer Genome Atlas |
THPA | The Human Protein Atlas |
TME | Tumor microenvironment |
ECM | Extracellular matrix |
CNA | Copy number alterations |
GMM | Gaussian mixture model |
EM | Expectation maximization |
SD | Standard deviation |
Appendix A
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Kääriäinen, A.; Pesola, V.; Dittmann, A.; Kontio, J.; Koivunen, J.; Pihlajaniemi, T.; Izzi, V. Machine Learning Identifies Robust Matrisome Markers and Regulatory Mechanisms in Cancer. Int. J. Mol. Sci. 2020, 21, 8837. https://doi.org/10.3390/ijms21228837
Kääriäinen A, Pesola V, Dittmann A, Kontio J, Koivunen J, Pihlajaniemi T, Izzi V. Machine Learning Identifies Robust Matrisome Markers and Regulatory Mechanisms in Cancer. International Journal of Molecular Sciences. 2020; 21(22):8837. https://doi.org/10.3390/ijms21228837
Chicago/Turabian StyleKääriäinen, Anni, Vilma Pesola, Annalena Dittmann, Juho Kontio, Jarkko Koivunen, Taina Pihlajaniemi, and Valerio Izzi. 2020. "Machine Learning Identifies Robust Matrisome Markers and Regulatory Mechanisms in Cancer" International Journal of Molecular Sciences 21, no. 22: 8837. https://doi.org/10.3390/ijms21228837