Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data.

ABSTRACT: Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. Although classical machine learning techniques have successfully been applied to find informative genes and to predict class labels for new samples, common restrictions of microarray analysis such as small sample sizes, a large attribute space and high noise levels still limit its scientific and clinical applications. Increasing the interpretability of prediction models while retaining a high accuracy would help to exploit the information content in microarray data more effectively. For this purpose, we evaluate our rule-based evolutionary machine learning systems, BioHEL and GAssist, on three public microarray cancer datasets, obtaining simple rule-based models for sample classif

SUBMITTER: Glaab E 

PROVIDER: S-EPMC3394775 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature