论文标题
对癌症网络的复杂系统方法的综述
A Review of Complex Systems Approaches to Cancer Networks
论文作者
论文摘要
癌症仍然是北美与疾病有关的小儿死亡的主要原因。复杂系统的新兴领域已将癌症网络重新定义为具有棘手算法复杂性的计算系统。在此，将肿瘤及其异质表型讨论为具有多个奇怪吸引子的动力学系统。讨论了机器学习，网络科学和算法信息动态，作为当前的癌症网络重建工具。提出了深度学习体系结构和计算流体模型，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了癌细胞的决策。
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.
