Guest Editorial: Introduction to the Special Issue on Machine Learning for Microarray Bioinformatics

One of the main challenges in computational biology is the revelation and interpretation of the rich genomic information underlying cancer biology and to facilitating molecular classification and prediction of cancers and responses to therapies. Genomic sequencing and gene expression technologies have been widely recognized as vital approaches to modern drug design and disease classification. With the recent advances in DNA microarray technologies, it has become possible to measure the expression level of thousands of genes simultaneously. However, the large number of genes together with the complexity of gene expression patterns and sequences make interpreting the million of biological measurements a challenging task. This special issue showcases various machine learning approaches to meeting this challenge. The areas in which this special issue is focusing on can be categorized as follows.