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.

中文翻译：

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全球功能基因组格局的计算预测：应用，方法和挑战。

技术的进步导致高通量功能基因组数据的爆炸性增长。利用不同数据类型之间的相关性，可以从其他数据类型中预测一种功能基因组数据类型。预测工具对于了解不同功能基因组信号之间的关系非常有用。当由于资源或技术限制而无法获得实验数据时，它们还提供了一种经济高效的解决方案来推断未知的功能基因组概况。预测的数据可用于生成假设，确定目标的优先级，解释疾病变体，促进数据集成，质量控制以及许多其他目的。本文回顾了预测方法在功能基因组学中的各种应用，讨论了分析难题，