Wang Anyou, Sarwal Minnie M
Department of Surgery, Division of MultiOrgan Transplantation, University of California San Francisco , San Francisco, CA , USA.
Front Immunol. 2015 Sep 8;6:458. doi: 10.3389/fimmu.2015.00458. eCollection 2015.
Translational medicine offers a rich promise for improved diagnostics and drug discovery for biomedical research in the field of transplantation, where continued unmet diagnostic and therapeutic needs persist. Current advent of genomics and proteomics profiling called "omics" provides new resources to develop novel biomarkers for clinical routine. Establishing such a marker system heavily depends on appropriate applications of computational algorithms and software, which are basically based on mathematical theories and models. Understanding these theories would help to apply appropriate algorithms to ensure biomarker systems successful. Here, we review the key advances in theories and mathematical models relevant to transplant biomarker developments. Advantages and limitations inherent inside these models are discussed. The principles of key -computational approaches for selecting efficiently the best subset of biomarkers from high--dimensional omics data are highlighted. Prediction models are also introduced, and the integration of multi-microarray data is also discussed. Appreciating these key advances would help to accelerate the development of clinically reliable biomarker systems.
转化医学为移植领域的生物医学研究改善诊断和药物发现带来了巨大希望，该领域中诊断和治疗需求仍未得到满足。当前所谓的“组学”（基因组学和蛋白质组学分析）的出现为开发临床常规用的新型生物标志物提供了新资源。建立这样一个标志物系统在很大程度上依赖于计算算法和软件的恰当应用，而这些算法和软件基本上是基于数学理论和模型的。理解这些理论将有助于应用恰当的算法以确保生物标志物系统的成功。在此，我们综述与移植生物标志物开发相关的理论和数学模型的关键进展。讨论了这些模型内在的优点和局限性。强调了从高维组学数据中高效选择最佳生物标志物子集的关键计算方法的原理。还介绍了预测模型，并讨论了多微阵列数据的整合。了解这些关键进展将有助于加速临床可靠生物标志物系统的开发。
