Báo cáo hóa học: " Identification and classification of human cytomegalovirus capsids in textured electron micrographs using deformed template matching"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Identification and classification of human cytomegalovirus capsids in textured electron micrographs using deformed template matching | Virology Journal BioMed Central Open Access Methodology Identification and classification of human cytomegalovirus capsids in textured electron micrographs using deformed template matching Martin Ryner1 2 Jan-Olov Stromberg2 Cecilia Soderberg-Nauclér1 and Mohammed Homman-Loudiyi 1 Address Department of Medicine Centre for Molecular Medicine Karolinska Instituted Stockholm Sweden and 2Department of Mathematics and NADA Royal Institute of Technology Stockholm Sweden Email Martin Ryner - martinrr@ Jan-Olov Stromberg - jostromb@ Cecilia Soderberg-Nauclér - Mohammed Homman-Loudiyi - Corresponding author Published 18 August 2006 Received 03 May 2006 Accepted 18 August 2006 Virologyjournal 2006 3 57 doi I86 I743-422X-3-57 This article is available from http content 3 1 57 2006 Ryner et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background Characterization of the structural morphology of virus particles in electron micrographs is a complex task but desirable in connection with investigation of the maturation process and detection of changes in viral particle morphology in response to the effect of a mutation or antiviral drugs being applied. Therefore we have here developed a procedure for describing and classifying virus particle forms in electron micrographs based on determination of the invariant characteristics of the projection of a given virus structure. The template for the virus particle is created on the basis of information obtained from a small training set of electron micrographs and is then employed to classify and quantify similar structures of interest in an unlimited number of electron micrographs by a process .

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