摘要翻译：
在北美，癌症仍然是与疾病相关的儿童死亡的主要原因。复杂系统的新兴领域将癌症网络重新定义为一个具有难以解决的算法复杂性的计算系统。本文将肿瘤及其异质性表型作为具有多个奇怪吸引子的动力系统进行讨论。讨论了机器学习、网络科学和算法信息动力学作为当前癌症网络重建的工具。为了更好地预测癌症生态系统中的基因表达模式，提出了深度学习架构和计算流体模型。在复杂系统和复杂性理论的框架内研究了癌细胞决策问题。
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英文标题：
《A Review of Complex Systems Approaches to Cancer Networks》
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作者：
Abicumaran Uthamacumaran
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最新提交年份：
2021
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类：Physics        物理学
二级分类：Chaotic Dynamics        混沌动力学
分类描述：Dynamical systems, chaos, quantum chaos, topological dynamics, cycle expansions, turbulence, propagation
动力系统，混沌，量子混沌，拓扑动力学，循环展开，湍流，传播
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英文摘要：
  Cancers remain the lead cause of disease-related, pediatric death in North America. The emerging field of complex systems has redefined cancer networks as a computational system with intractable algorithmic complexity. Herein, a tumor and its heterogeneous phenotypes are discussed as dynamical systems having multiple, strange attractors. Machine learning, network science and algorithmic information dynamics are discussed as current tools for cancer network reconstruction. Deep Learning architectures and computational fluid models are proposed for better forecasting gene expression patterns in cancer ecosystems. Cancer cell decision-making is investigated within the framework of complex systems and complexity theory.
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PDF链接：
https://arxiv.org/pdf/2009.12693