| Chapter title |
Peptide bioinformatics: peptide classification using peptide machines.
|
|---|---|
| Chapter number | 9 |
| Book title |
Artificial Neural Networks
|
| Published in |
Methods in molecular biology, January 2008
|
| DOI | 10.1007/978-1-60327-101-1_9 |
| Pubmed ID | |
| Book ISBNs |
978-1-58829-718-1, 978-1-60327-101-1
|
| Authors |
Yang, Zheng Rong, David J. Livingstone, Zheng Rong Yang |
| Abstract |
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. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 8% |
| Unknown | 11 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Other | 2 | 17% |
| Student > Bachelor | 2 | 17% |
| Librarian | 1 | 8% |
| Student > Doctoral Student | 1 | 8% |
| Lecturer | 1 | 8% |
| Other | 2 | 17% |
| Unknown | 3 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 3 | 25% |
| Nursing and Health Professions | 2 | 17% |
| Biochemistry, Genetics and Molecular Biology | 1 | 8% |
| Immunology and Microbiology | 1 | 8% |
| Chemistry | 1 | 8% |
| Other | 0 | 0% |
| Unknown | 4 | 33% |