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

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conference contribution
posted on 2008-01-01, 00:00 authored by Abbas KouzaniAbbas Kouzani
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.

History

Pagination

3926 - 3932

Location

Hong Kong

Open access

  • Yes

Start date

2008-06-01

End date

2008-06-06

ISBN-13

9781424418213

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2008, IEEE

Editor/Contributor(s)

J Wang

Title of proceedings

WCCI 2008 : IEEE World Congress on Computational Intelligence

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