Application of Support Vector Machines in Fungal Genome and Proteome Annotation

Support Vector Machines (SVM) is a statistical machine learning algorithm that has been used extensively in the past 3 years in computational biology. SVM has been widely used to detect, classify, and predict complex biological patterns. SVMs have been widely applied to many areas of bioinformatics, including protein function prediction, functional site recognition, transcription initiation site prediction, and gene expression data classification. This chapter gives a brief overview of SVM algorithm along with its specific applications in fungal genome and proteome annotations.