- Review -

Volume 9

Full article

Classification of information fusion methods in systems biology

Jane Synnergren*, Björn Olsson and Jonas Gamalielsson

Systems Biology Research Centre, School of Life Sciences, University of Skövde, SE-541 28 Skövde, Sweden

* Corresponding author
   Email: jane.synnergren@his.se


Edited by E. Wingender; received August 04, 2008; revised February 21, 2009; accepted February 23, 2009; published April 15, 2009

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