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

Author(s): Honda, H. | Kobayashi, T. | Takahashi, H. |

Year: 2005

Citation: Bioinformatics, Vol. 21, 179-186

Abstract: 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 the 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.

Topics: Machine Learning, Applications: Medical Diagnosis, Models: ART 2 / Fuzzy ART,

PDF download

Cross References