Biocenter, Section for Bioinformatics, Innsbruck Medical University, Schöpfstrasse 45, 6020, Innsbruck, Austria.
Pharmacol Ther. 2010 Dec;128(3):488-98. doi: 10.1016/j.pharmthera.2010.08.012. Epub 2010 Sep 9.
The quest for new cancer biomarkers and targets for therapy requires not only the aggregation and analysis of heterogeneous biomolecular data but also integration of clinical data. In this review we highlight information technology solutions for the integration of biomolecular and clinical data and focus on a solution at the departmental level, i.e., decentralized and medium-scale solution for groups of labs working on a specific topic. Both, hardware and software requirements are described as well as bioinformatics methods and tools for the data analysis. The highlighted IT solutions include storage architecture, high-performance computing, and application servers. Additionally, following computational approaches for data integration are reviewed: data aggregation, integrative data analysis including methodological aspects as well as examples, biomolecular pathways and network reconstruction, and mathematical modelling. Finally, a case study in cancer immunology including the used computational methods is shown, demonstrating how IT solutions for integrating biomolecular and clinical data can help to identify new cancer biomarkers for improving diagnosis and predicting clinical outcome.
寻找新的癌症生物标志物和治疗靶点不仅需要聚合和分析异质生物分子数据，还需要整合临床数据。在这篇综述中，我们重点介绍了生物分子和临床数据集成的信息技术解决方案，并关注部门层面的解决方案，即针对特定主题的实验室小组的分散式和中等规模的解决方案。本文还描述了硬件和软件要求以及用于数据分析的生物信息学方法和工具。所强调的 IT 解决方案包括存储架构、高性能计算和应用服务器。此外，还回顾了用于数据集成的计算方法：数据聚合、包括方法学方面以及示例的综合数据分析、生物分子途径和网络重建以及数学建模。最后，展示了癌症免疫学的案例研究，包括所使用的计算方法，证明了用于整合生物分子和临床数据的 IT 解决方案如何帮助识别新的癌症生物标志物，以改善诊断和预测临床结果。
