New technologies have enabled the investigation of biology and human health
at an unprecedented scale and in multiple dimensions. These dimensions include
a myriad of properties describing genome, epigenome, transcriptome, microbiome,
phenotype, and lifestyle. No single data type, however, can capture the
complexity of all the factors relevant to understanding a phenomenon such as a
disease. Integrative methods that combine data from multiple technologies have
thus emerged as critical statistical and computational approaches. The key
challenge in developing such approaches is the identification of effective
models to provide a comprehensive and relevant systems view. An ideal method
can answer a biological or medical question, identifying important features and
predicting outcomes, by harnessing heterogeneous data across several dimensions
of biological variation. In this Review, we describe the principles of data
integration and discuss current methods and available implementations. We
provide examples of successful data integration in biology and medicine.
Finally, we discuss current challenges in biomedical integrative methods and
our perspective on the future development of the field.