Guest Editorial: Introduction to the special issue on machine learning for microarray bioinformatics

This special issue of the journal contains some significant information associated with machine learning for microarray bioinformatics. Clustering of gene expression data can be used to identify groups of genes or groups of samples that exhibit similar expression patterns and its applications includes disease diagnoses, gene identification, and discovery. The accuracy of the gene expression level extracted from the spots on the microarrays ensures the reliability of clinical applications of microarrays. Wang and co-workers propose a fuzzy-clustering spot-segmentation method that can handle array spots with complex shapes like donuts and scratches. Understanding the dynamic processes of gene expression can be used to optimize biological process for the biotech industry. Kramer and Xu present a method for inferring a continuous model from discrete gene measurements.