Abstract
Cyanobacteria are photosynthetic organisms that are credited with both the creation and replenishment of the oxygen-rich atmosphere, and are also responsible for more than half of the primary production on earth. Despite their crucial evolutionary and environmental roles, the study of these organisms has lagged behind other model organisms. This paper presents preliminary results on our ongoing research to unravel the biological interactions occurring within cyanobacteria. We develop an analysis framework that leverages recently developed bioinformatics and machine learning tools, such as genome-wide sequence matching based annotation, gene ontology analysis, cluster analysis and dynamic Bayesian network. Together, these tools allow us to overcome the lack of knowledge of less well-studied organisms, and reveal interesting relationships among their biological processes. Experiments on the Cyanothece bacterium demonstrate the practicability and usefulness of our approach.
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Vinh, N.X., Chetty, M., Coppel, R., Wangikar, P.P. (2011). Dynamic Bayesian Network Modeling of Cyanobacterial Biological Processes via Gene Clustering. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_12
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DOI: https://doi.org/10.1007/978-3-642-24955-6_12
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