Proposal of new gene filtering method, BagPART, for gene expression analysis with small sample

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

Year: 2008

Citation: JOURNAL OF BIOSCIENCE AND BIOENGINEERING Volume: 105 Issue: 1 Pages: 81-84

Abstract: A significant problem in gene expression analysis is that the sample size is substantially lower than the number of genes. Bagging is an effective method of solving this problem in the case of small sample datasets. We have devised a combination method, called the BagPART filtering method, that uses the projective adaptive resonance theory (PART) to select important genes and achieve a binary classification more accurately (p<10(-10)) than conventional methods, particularly when the sample size is small.

Topics: Machine Learning, Applications: Medical Diagnosis, Models: ART 1,

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Cross References


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


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