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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.

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Abstract

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
Language:English
Date:14 April 2007
Deposited On:11 Feb 2008 12:14
Last Modified:28 Nov 2013 01:58
Publisher:Elsevier
ISSN:1047-8477
Publisher DOI:10.1016/j.jsb.2007.04.003
PubMed ID:17532228
Citations:Web of Science®. Times Cited: 27
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Scopus®. Citation Count: 26

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