Clustering short time series gene expression data
Bioinformatics
Towards an automated analysis of biomedical abstracts
DILS'06 Proceedings of the Third international conference on Data Integration in the Life Sciences
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Systems biology aims to understand the behavior of and interaction between various components of the living cell, such as genes, proteins, and metabolites. A large number of components are involved in these complex systems and the diversity of relationships between the components can be overwhelming, and there is therefore a need for analysis methods incorporating data integration. We here present a method for exploring gene regulatory mechanisms which integrates various types of data to assist the identification of important components in gene regulation mechanisms. By first analyzing gene expression data, a set of differentially expressed genes is selected. These genes are then further investigated by combining various types of biological information, such as clustering results, promoter sequences, binding sites, transcription factors and other previously published information regarding the selected genes. Inspired by Information Fusion research, we also mapped functions of the proposed method to the well-known OODA-model to facilitate application of this data integration method in other research communities. We have successfully applied the method to genes identified as differentially expressed in human embryonic stem cells at different stages of differentiation towards cardiac cells. We identified 15 novel motifs that may represent important binding sites in the cardiac cell linage.