Applying Machine Learning Techniques to Analysis of Gene Expression Data: Cancer Diagnosis [chapter]

Kyu-Baek Hwang, Dong-Yeon Cho, Sang-Wook Park, Sung-Dong Kim, Byoung-Tak Zhang
2002 Methods of Microarray Data Analysis  
Classification of patient samples is a crucial aspect of cancer diagnosis. DNA hybridization arrays simultaneously measure the expression levels of thousands of genes and it has been suggested that gene expression may provide the additional information needed to improve cancer classification and diagnosis. This paper presents methods for analyzing gene expression data to classify cancer types. Machine learning techniques, such as Bayesian networks, neural trees, and radial basis function (RBF)
more » ... etworks, are used for the analysis of the CAMDA Data Set 2. These techniques have their own properties including the ability of finding important genes for cancer classification, revealing relationships among genes, and classifying cancer. This paper reports on comparative evaluation of the experimental results of these methods.
doi:10.1007/978-1-4615-0873-1_13 fatcat:ftaxuah6qnfnvlzzpmj6nz75q4