id: 05984994
dt: a
an: 05984994
au: Vinh, Nguyen Xuan; Chetty, Madhu; Coppel, Ross; Wangikar, Pramod P.
ti: Dynamic Bayesian network modeling of cyanobacterial biological processes
    via gene clustering.
so: Lu, Bao-Liang (ed.) et al., Neural information processing. 18th
    international conference, ICONIP 2011, Shanghai, China, November
    13‒17, 2011. Proceedings, Part I. Berlin: Springer (ISBN
    978-3-642-24954-9/pbk). Lecture Notes in Computer Science 7062, 97-106
    (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: cyanobacteria; cyanothece; dynamic Bayesian network; clustering; gene
    ontology; gene regulatory network
ci: 
li: doi:10.1007/978-3-642-24955-6_12
ab: Summary: 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.
rv: