Abstract: Biological systems are extremely complex and often involve thousands
of interacting components. Despite all efforts, many complex biological systems
are still poorly understood. However, over the past few years high-throughput
technologies have generated large amounts of biological data, now requiring
advanced bioinformatic algorithms for interpretation into valuable biological
information. Due to these high-throughput technologies, the study of biological
systems has evolved from focusing on single components (e.g. genes) to
encompassing large sets of components (e.g. all genes in an entire genome),
with the aim to elucidate their interdependences in various biological
processes. In addition, there is also an increasing need for integrative
analysis, where knowledge about the biological system is derived by data
fusion, using heterogeneous data sets as input. We here review representative
examples of bioinformatic methods for fusion-oriented interpretation of
multiple heterogeneous biological data, and propose a classification into three
categories of tasks that they address: data extraction, data integration and
data fusion. The aim of this classification is to facilitate the exchange of
methods between systems biology and other information fusion application
areas.
Keywords: Information fusion, data fusion, data integration, systems biology
