Zhou Weiqiang, Sherwood Ben, Ji Hongkai
Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Md., USA.
Hum Hered. 2016;81(2):88-105. doi: 10.1159/000450827. Epub 2017 Jan 12.
Technological advances have led to an explosive growth of high-throughput functional genomic data. Exploiting the correlation among different data types, it is possible to predict one functional genomic data type from other data types. Prediction tools are valuable in understanding the relationship among different functional genomic signals. They also provide a cost-efficient solution to inferring the unknown functional genomic profiles when experimental data are unavailable due to resource or technological constraints. The predicted data may be used for generating hypotheses, prioritizing targets, interpreting disease variants, facilitating data integration, quality control, and many other purposes. This article reviews various applications of prediction methods in functional genomics, discusses analytical challenges, and highlights some common and effective strategies used to develop prediction methods for functional genomic data.
技术进步导致了高通量功能基因组数据的爆炸式增长。利用不同数据类型之间的相关性，可以从其他数据类型预测一种功能基因组数据类型。预测工具对于理解不同功能基因组信号之间的关系很有价值。当由于资源或技术限制无法获得实验数据时，它们还为推断未知的功能基因组概况提供了一种经济高效的解决方案。预测数据可用于生成假设、确定目标优先级、解释疾病变异、促进数据整合、质量控制以及许多其他目的。本文综述了预测方法在功能基因组学中的各种应用，讨论了分析挑战，并强调了一些用于开发功能基因组数据预测方法的常见有效策略。
