Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data.
Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data.
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DOI：
10.1517/17530059.2012.718329
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发表时间：
2013-01
期刊：
影响因子：
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通讯作者：
Rodland KD
中科院分区：
文献类型：
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作者：
McDermott JE;Wang J;Mitchell H;Webb-Robertson BJ;Hafen R;Ramey J;Rodland KD
能够全面分析基因、转录物、蛋白质和其他重要生物分子的高通量技术的出现为鉴定疾病过程的分子标志物提供了前所未有的机会。然而，它同时也使从这些复杂的数据集中提取生物过程的有意义的分子特征的问题变得复杂。生物标志物发现和表征的过程为更复杂的方法提供了机会，以整合纯粹的统计和基于专家知识的方法。在这篇综述中，我们将介绍从复杂的组学数据集发现生物标志物的当前实践的例子，以及在推导有效和有用的疾病特征时遇到的挑战。然后，我们将提出一个高层次的审查数据驱动（统计）和知识为基础的方法应用于生物标志物的发现，突出了一些目前的努力，联合收割机这两种不同的方法。有效的，可重复的和客观的工具，用于结合数据驱动和基于知识的方法来识别疾病的预测特征，是生物标志物领域未来成功的关键。我们将描述我们对这个问题的可能方法的建议，包括评估生物标志物的指标。
The advent of high throughput technologies capable of comprehensive analysis of genes, transcripts, proteins and other significant biological molecules has provided an unprecedented opportunity for the identification of molecular markers of disease processes. However, it has simultaneously complicated the problem of extracting meaningful molecular signatures of biological processes from these complex datasets. The process of biomarker discovery and characterization provides opportunities for more sophisticated approaches to integrating purely statistical and expert knowledge-based approaches. In this review we will present examples of current practices for biomarker discovery from complex omic datasets and the challenges that have been encountered in deriving valid and useful signatures of disease. We will then present a high-level review of data-driven (statistical) and knowledge-based methods applied to biomarker discovery, highlighting some current efforts to combine the two distinct approaches. Effective, reproducible and objective tools for combining data-driven and knowledge-based approaches to identify predictive signatures of disease are key to future success in the biomarker field. We will describe our recommendations for possible approaches to this problem including metrics for the evaluation of biomarkers.