abstract = "The rapid expansion of methods for measuring
biological data ranging from DNA sequence variations to
mRNA expression and protein abundance presents the
opportunity to use multiple types of information
jointly in the study of human health and disease.
Organisms are complex systems that integrate inputs at
myriad levels to arrive at an observable phenotype.
Therefore, it is essential that questions concerning
the etiology of phenotypes as complex as common human
diseases take the systemic nature of biology into
account, and integrate the information provided by each
data type in a manner analogous to the operation of the
body itself. While limited in scope, the initial forays
into the joint analysis of multiple data types have
yielded interesting results that would not have been
reached had only one type of data been considered.
These early successes, along with the aforementioned
theoretical appeal of data integration, provide impetus
for the development of methods for the parallel,
high-throughput analysis of multiple data types. The
idea that the integrated analysis of multiple data
types will improve the identification of biomarkers of
clinical endpoints, such as disease susceptibility, is
presented as a working hypothesis.",