癌症网络的复杂系统研究进展
A Review of Complex Systems Approaches to Cancer Networks
摘要：
在北美，癌症仍然是与疾病相关的儿科死亡的主要原因。新兴的复杂系统领域已经将癌症网络重新定义为具有难以处理的算法复杂性的计算系统。在这里，肿瘤和它的异质性表型被讨论为具有多个奇怪吸引子的动力系统。讨论了机器学习、网络科学和算法信息动力学作为癌症网络重建的当前工具。提出了深度学习架构和计算流体模型，以更好地预测癌症生态系统中的基因表达模式。在复杂系统和复杂性理论的框架内研究癌细胞决策。
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
