Baumgartner Christian, Osl Melanie, Netzer Michael, Baumgartner Daniela
Research Group for Clinical Bioinformatics, Institute of Electrical, Electronic and Bioengineering, University for Health Sciences, Medical Informatics and Technology (UMIT), Hall in Tirol, Austria.
J Clin Bioinforma. 2011 Jan 20;1(1):2. doi: 10.1186/2043-9113-1-2.
The search and validation of novel disease biomarkers requires the complementary power of professional study planning and execution, modern profiling technologies and related bioinformatics tools for data analysis and interpretation. Biomarkers have considerable impact on the care of patients and are urgently needed for advancing diagnostics, prognostics and treatment of disease. This survey article highlights emerging bioinformatics methods for biomarker discovery in clinical metabolomics, focusing on the problem of data preprocessing and consolidation, the data-driven search, verification, prioritization and biological interpretation of putative metabolic candidate biomarkers in disease. In particular, data mining tools suitable for the application to omic data gathered from most frequently-used type of experimental designs, such as case-control or longitudinal biomarker cohort studies, are reviewed and case examples of selected discovery steps are delineated in more detail. This review demonstrates that clinical bioinformatics has evolved into an essential element of biomarker discovery, translating new innovations and successes in profiling technologies and bioinformatics to clinical application.
新型疾病生物标志物的搜索与验证需要专业研究规划与执行、现代分析技术以及用于数据分析与解读的相关生物信息学工具的互补力量。生物标志物对患者护理有重大影响，对于推进疾病的诊断、预后评估和治疗而言迫切需要。这篇综述文章重点介绍了临床代谢组学中用于生物标志物发现的新兴生物信息学方法，聚焦于数据预处理与整合问题、假定代谢候选生物标志物在疾病中的数据驱动搜索、验证、优先级排序及生物学解读。特别地，本文回顾了适用于从最常用实验设计类型（如病例对照或纵向生物标志物队列研究）收集的组学数据的数据挖掘工具，并更详细地描述了选定发现步骤的案例。这篇综述表明，临床生物信息学已发展成为生物标志物发现的关键要素，将分析技术和生物信息学的新创新与成功转化为临床应用。
