Johnson David, McKeever Steve, Stamatakos Georgios, Dionysiou Dimitra, Graf Norbert, Sakkalis Vangelis, Marias Konstantinos, Wang Zhihui, Deisboeck Thomas S
Department of Computer Science, University of Oxford, Oxford, UK.
Cancer Inform. 2013 May 7;12:115-24. doi: 10.4137/CIN.S11583. Print 2013.
This paper discusses the need for interconnecting computational cancer models from different sources and scales within clinically relevant scenarios to increase the accuracy of the models and speed up their clinical adaptation, validation, and eventual translation. We briefly review current interoperability efforts drawing upon our experiences with the development of in silico models for predictive oncology within a number of European Commission Virtual Physiological Human initiative projects on cancer. A clinically relevant scenario, addressing brain tumor modeling that illustrates the need for coupling models from different sources and levels of complexity, is described. General approaches to enabling interoperability using XML-based markup languages for biological modeling are reviewed, concluding with a discussion on efforts towards developing cancer-specific XML markup to couple multiple component models for predictive in silico oncology.
本文讨论了在临床相关场景中连接来自不同来源和尺度的计算癌症模型的必要性，以提高模型的准确性并加速其临床应用、验证及最终转化。我们借鉴在欧盟委员会多个癌症虚拟生理人计划项目中开发肿瘤预测计算机模型的经验，简要回顾了当前的互操作性工作。描述了一个临床相关场景，即针对脑肿瘤建模，该场景说明了耦合来自不同来源和不同复杂程度模型的必要性。回顾了使用基于XML的标记语言实现生物建模互操作性的一般方法，最后讨论了开发癌症特异性XML标记以耦合多个组件模型用于肿瘤预测计算机模拟的工作进展情况。
