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Subcellular localisation of proteins in fluorescent microscope images using a random forest

Kouzani, Abbas 2008, Subcellular localisation of proteins in fluorescent microscope images using a random forest, in WCCI 2008 : IEEE World Congress on Computational Intelligence, IEEE, Piscataway, N.J., pp. 3926-3932.

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Title Subcellular localisation of proteins in fluorescent microscope images using a random forest
Author(s) Kouzani, Abbas
Conference name IEEE World Congress on Computational Intelligence (2008 : Hong Kong)
Conference location Hong Kong
Conference dates 1-6 June 2008
Title of proceedings WCCI 2008 : IEEE World Congress on Computational Intelligence
Editor(s) Wang, Jun
Publication date 2008
Conference series IEEE World Congress on Computational Intelligence
Start page 3926
End page 3932
Total pages 7
Publisher IEEE
Place of publication Piscataway, N.J.
Summary This paper presents a system that employs random forests to formulate a method for subcellular localisation of proteins. A random forest is an ensemble learner that grows classification trees. Each tree produces a classification decision, and an integrated output is calculated. The system classifies the protein-localisation patterns within fluorescent microscope images. 2D images of HeLa cells that include all major classes of subcellular structures, and the associated feature set are used. The performance of the developed system is compared against that of the support vector machine and decision tree approaches. Three experiments are performed to study the influence of the training and test set size on the performance of the examined methods. The calculated classification errors and execution times are presented and discussed. The lowest classification error (2.9%) has been produced by the developed system.
ISBN 9781424418213
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 920203 Diagnostic Methods
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2008, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30018284

Document type: Conference Paper
Collections: School of Engineering and Information Technology
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