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Bioinformatics Advance Access published online on August 12, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth473
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received April 13, 2004
Revised August 6, 2004
Accepted August 8, 2004

Article

Construction of robust prognostic predictors by using projective adaptive resonance theory as a gene filtering method

Hiro Takahashi 1, Takeshi Kobayashi 1, Hiroyuki Honda 1*

1 Department of Biotechnology, School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan

* To whom correspondence should be addressed. E-mail: honda{at}nubio.nagoya-u.ac.jp.


  Abstract

Motivation: For establishing prognostic predictors of various diseases using DNA microarray analysis technology, it is desired to selectively find significant genes for constructing the prognostic model and also necessary to eliminate nonspecific genes or genes with error before constructing the model.

Result: We applied projective adaptive resonance theory (PART) to gene screening for DNA microarray data. Genes selected by PART were subjected to our FNN-SWEEP modeling method for construction of a cancer class prediction model. The model performance was evaluated through comparison with a conventional screening signal-to-noise (S2N) method or nearest shrunken centroids (NSC) method. The FNN-SWEEP predictor with PART screening could discriminate classes of acute leukemia in blinded data with 97.1% accuracy and classes of lung cancer with 90.0% accuracy, while the predictor with S2N was only 85.3% and 70.0% or the predictor with NSC was 88.2% and 90.0%, respectively. The results have proven that PART was superior for gene screening.

Availability: The software is available from the authors upon request.


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H. Takahashi and H. Honda
Modified signal-to-noise: a new simple and practical gene filtering approach based on the concept of projective adaptive resonance theory (PART) filtering method
Bioinformatics, July 1, 2006; 22(13): 1662 - 1664.
[Abstract] [Full Text] [PDF]


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