Peptide bioinformatics: peptide classification using peptide machines.
Peptides scanned from whole protein sequences are the core information for many peptide bioinformatics research such as functional site prediction, protein structure identification, and protein function recognition. In these applications, we normally need to assign a peptide to one of the given categories using a computer model. They are therefore referred to as peptide classification applications. Among various machine learning approaches, including neural networks, peptide machines have demonstrated excellent performance in many applications. This chapter discusses the basic concepts of peptide classification, commonly used feature extraction methods, three peptide machines, and some important issues in peptide classification.
Yang
Zheng Rong
ZR
School of Biosciences, University of Exeter, UK.
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001695:Biology / Q000379:methods; D019295:Computational Biology / Q000379:methods* / Q000639:trends; D006801:Humans; D015233:Models, Statistical; D009154:Mutation; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D010455:Peptides / Q000032:analysis* / Q000737:chemistry* / Q000145:classification; D011506:Proteins / Q000737:chemistry* / Q000145:classification
D010455:Peptides; D011506:Proteins