18. Kernel Methods and Applications in Bioinformatics

The kernel technique is a powerful tool for constructing new pattern analysis methods. Kernel engineering provides a general approach to incorporating domain knowledge and dealing with discrete data structures. Kernel methods, especially the support vector machine (SVM), have been extensively applied in the bioinformatics field, achieving great successes. Meanwhile, the development of kernel methods has also been strongly driven by various challenging bioinformatic problems. This chapter aims to give a concise and intuitive introduction to the basic principles of the kernel technique, and demonstrate how it can be applied to solve problems with uncommon data types in bioinformatics. Section 18.1 begins with the product features to give an intuitive idea of kernel functions, then presents the definition and some properties of kernel functions, and then devotes a subsection to a brief review of kernel engineering and its applications to bioinformatics. Section 18.2 describes the standard SVM algorithm. Finally, Sect. 18.3 illustrates how kernel methods can be used to address the peptide identification and the protein homology prediction problems in bioinformatics, while Sect. 18.4 concludes.