-
- Zheng Rong Yang.
- School of Biosciences, University of Exeter, UK.
- Methods Mol. Biol. 2008 Jan 1; 458: 159-83.
AbstractPeptides 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.
Notes
Knowledge, pearl, summary or comment to share?You can also include formatting, links, images and footnotes in your notes
- Simple formatting can be added to notes, such as
*italics*,_underline_or**bold**. - Superscript can be denoted by
<sup>text</sup>and subscript<sub>text</sub>. - Numbered or bulleted lists can be created using either numbered lines
1. 2. 3., hyphens-or asterisks*. - Links can be included with:
[my link to pubmed](http://pubmed.com) - Images can be included with:
 - For footnotes use
[^1](This is a footnote.)inline. - Or use an inline reference
[^1]to refer to a longer footnote elseweher in the document[^1]: This is a long footnote..