Yu Peter, Artz David, Warner Jeremy
From the Department of Hematology and Department of Oncology, Palo Alto Medical Foundation, Mountain View, CA; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY; Department of Medicine and Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN.
Am Soc Clin Oncol Educ Book. 2014:225-31. doi: 10.14694/EdBook_AM.2014.34.225.
ASCO's vision for cancer care in 2030 is built on the expanding importance of panomics and big data, and envisions enabling better health for patients with cancer by the rapid transformation of systems biology knowledge into cancer care advances. This vision will be heavily dependent on the use of health information technology for computational biology and clinical decision support systems (CDSS). Computational biology will allow us to construct models of cancer biology that encompass the complexity of cancer panomics data and provide us with better understanding of the mechanisms governing cancer behavior. The Agency for Healthcare Research and Quality promotes CDSS based on clinical practice guidelines, which are knowledge bases that grow too slowly to match the rate of panomic-derived knowledge. CDSS that are based on systems biology models will be more easily adaptable to rapid advancements and translational medicine. We describe the characteristics of health data representation, a model for representing molecular data that supports data extraction and use for panomic-based clinical research, and argue for CDSS that are based on systems biology and are algorithm-based.
美国临床肿瘤学会（ASCO）对2030年癌症护理的愿景建立在泛组学和大数据日益重要的基础之上，设想通过将系统生物学知识迅速转化为癌症护理进展，为癌症患者带来更健康的生活。这一愿景将极大地依赖于健康信息技术在计算生物学和临床决策支持系统（CDSS）中的应用。计算生物学将使我们能够构建癌症生物学模型，该模型涵盖癌症泛组学数据的复杂性，并让我们更好地理解控制癌症行为的机制。医疗保健研究与质量局基于临床实践指南推广临床决策支持系统，而这些知识库的增长速度过慢，无法跟上泛组学衍生知识的增长速度。基于系统生物学模型的临床决策支持系统将更易于适应快速发展和转化医学。我们描述了健康数据表示的特征，这是一种用于表示分子数据的模型，支持数据提取并用于基于泛组学的临床研究，并主张采用基于系统生物学且基于算法的临床决策支持系统。
