IlliGAL Blogging

zmed posts an abstract on a paper by researchers at the University of Tokyo that uses parallel genetic algorithms to classify tissues.

Several machine learning approaches have been used to aid to understand the functions of genes. However, these tasks are made more difficult due to the noisy nature of array data and the overwhelming number of gene features. In this paper, we use the parallel genetic algorithm to filter out the informative genes relative to classification. By combing with the classification method proposed by Golub et al. and Slonim et al., we classify the data sets with tissues of different classes, and the preliminary results are presented in this paper.

The full text of the paper is available here.