Helmuth, J A; Burckhardt, C J; Koumoutsakos, P; Greber, U F; Sbalzarini, I F (2007). A novel supervised trajectory segmentation algorithm identifies distinct types of human adenovirus motion in host cells. Journal of Structural Biology, 159(3):347-358.
Full text not available from this repository.
Biological trajectories can be characterized by transient patterns that may provide insight into the interactions of the moving object with its immediate environment. The accurate and automated identification of trajectory motifs is important for the understanding of the underlying mechanisms. In this work, we develop a novel trajectory segmentation algorithm based on supervised support vector classification. The algorithm is validated on synthetic data and applied to the identification of trajectory fingerprints of fluorescently tagged human adenovirus particles in live cells. In virus trajectories on the cell surface, periods of confined motion, slow drift, and fast drift are efficiently detected. Additionally, directed motion is found for viruses in the cytoplasm. The algorithm enables the linking of microscopic observations to molecular phenomena that are critical in many biological processes, including infectious pathogen entry and signal transduction.
|Item Type:||Journal Article, refereed, original work|
|Communities & Collections:||07 Faculty of Science > Institute of Molecular Life Sciences|
|DDC:||570 Life sciences; biology|
|Date:||14 April 2007|
|Deposited On:||11 Feb 2008 13:14|
|Last Modified:||23 Nov 2012 17:50|
|WoS Citation Count:||17|
Users (please log in): suggest update or correction for this item
Repository Staff Only: item control page