Gene selection from microarray data for cancer classification - a machine learning approach

A DNA microarray can track the expression levels of thousands of genes simultaneously. Previous research has demonstrated that this technology can be useful in the classification of cancers. Cancer microarray data normally contains a small number of samples which have a large number of gene expression levels as features. To select relevant genes involved in different types of cancer remains a challenge. In order to extract useful gene information from cancer microarray data and reduce dimensionality, feature selection algorithms were systematically investigated in this study. Using a correlation-based feature selector combined with machine learning algorithms such as decision trees, na

• Type: Journal
• Year: 2005
• Where: CANDC
• Authors: Yu Wang 0008, Igor V. Tetko, Mark A. Hall, Eibe Frank, Axel Facius, Klaus F. X. Mayer, Hans-Werner Mewes
• Keywords: Cancer, Cancer Microarray Data, CANDC 2005, Emerging Technology, Expression Levels