Dominietto Marco, Tsinoremas Nicholas, Capobianco Enrico
Biomaterial Science Center, University of Basel, Basel, Switzerland; Institute for Biomedical Engineering, ETH and University of Zurich, Zurich, Switzerland.
Center for Computational Science, University of Miami, Miami, FL, USA.
Mol Oncol. 2015 Jan;9(1):1-16. doi: 10.1016/j.molonc.2014.08.013. Epub 2014 Sep 10.
Cancer is a multifactorial and heterogeneous disease. The corresponding complexity appears at multiple levels: from the molecular and the cellular constitution to the macroscopic phenotype, and at the diagnostic and therapeutic management stages. The overall complexity can be approximated to a certain extent, e.g. characterized by a set of quantitative phenotypic observables recorded in time-space resolved dimensions by using multimodal imaging approaches. The transition from measures to data can be made effective through various computational inference methods, including networks, which are inherently capable of mapping variables and data to node- and/or edge-valued topological properties, dynamic modularity configurations, and functional motifs. We illustrate how networks can integrate imaging data to explain cancer complexity, and assess potential pre-clinical and clinical impact.
癌症是一种多因素且异质性的疾病。相应的复杂性体现在多个层面：从分子和细胞构成到宏观表型，以及在诊断和治疗管理阶段。总体复杂性在一定程度上可以进行近似描述，例如通过使用多模态成像方法在时空分辨维度记录的一组定量表型可观测值来表征。从测量到数据的转换可以通过各种计算推理方法来实现，包括网络，网络本身能够将变量和数据映射到节点和/或边值拓扑属性、动态模块配置和功能基序。我们阐述了网络如何整合成像数据来解释癌症复杂性，并评估潜在的临床前和临床影响。
