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• W3179102385 abstract "Glioblastoma (GBM) is a highly morbid and lethal disease with poor prognosis. Their emergent properties such as cellular heterogeneity, therapy resistance, and self-renewal are largely attributed to the interactions between a subset of their population known as glioblastoma-derived stem cells (GSCs) and their microenvironment. Identifying causal patterns in the developmental trajectories between GSCs and the mature, well-differentiated GBM phenotypes remains a challenging problem in oncology. The paper presents a blueprint of complex systems approaches to infer attractor dynamics from the single-cell gene expression datasets of pediatric GBM and adult GSCs. These algorithms include Waddington landscape reconstruction, GANs (Generative Adversarial Networks), and fractal dimension analysis. Here I show, a Ro¨ssler-like strange attractor with a fractal dimension of roughly 1.7 emerged in all n = 12 patients’ GAN-reconstructed patterns. The findings suggest a strange attractor may be driving the complex dynamics and adaptive behaviors of GBM in signaling state-space." @default.
• W3179102385 created "2021-07-19" @default.
• W3179102385 creator A5073836717 @default.
• W3179102385 date "2021-10-07" @default.
• W3179102385 modified "2023-09-27" @default.
• W3179102385 title "Pattern Detection on Glioblastoma’s Waddington Landscape via Generative Adversarial Networks" @default.
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• W3179102385 doi "https://doi.org/10.1080/01969722.2021.1982160" @default.
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