Supplementary Material for: Computational Prediction of the Global Functional Genomic Landscape: Applications, Methods, and Challenges

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.