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Peptide Bioinformatics- Peptide Classification Using Peptide Machines
Peptides scanned from whole protein sequences are the core information for many peptide bioinformatics research subjects, 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...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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2009
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122642/ https://www.ncbi.nlm.nih.gov/pubmed/19065810 http://dx.doi.org/10.1007/978-1-60327-101-1_9 |
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author | Yang, Zheng Rong |
author_facet | Yang, Zheng Rong |
author_sort | Yang, Zheng Rong |
collection | PubMed |
description | Peptides scanned from whole protein sequences are the core information for many peptide bioinformatics research subjects, 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 compared with various conventional machine learning approaches 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. |
format | Online Article Text |
id | pubmed-7122642 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
record_format | MEDLINE/PubMed |
spelling | pubmed-71226422020-04-06 Peptide Bioinformatics- Peptide Classification Using Peptide Machines Yang, Zheng Rong Artificial Neural Networks Article Peptides scanned from whole protein sequences are the core information for many peptide bioinformatics research subjects, 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 compared with various conventional machine learning approaches 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. 2009 /pmc/articles/PMC7122642/ /pubmed/19065810 http://dx.doi.org/10.1007/978-1-60327-101-1_9 Text en © Humana Press, a part of Springer Science + Business Media, LLC 2008 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Yang, Zheng Rong Peptide Bioinformatics- Peptide Classification Using Peptide Machines |
title | Peptide Bioinformatics- Peptide Classification Using Peptide Machines |
title_full | Peptide Bioinformatics- Peptide Classification Using Peptide Machines |
title_fullStr | Peptide Bioinformatics- Peptide Classification Using Peptide Machines |
title_full_unstemmed | Peptide Bioinformatics- Peptide Classification Using Peptide Machines |
title_short | Peptide Bioinformatics- Peptide Classification Using Peptide Machines |
title_sort | peptide bioinformatics- peptide classification using peptide machines |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122642/ https://www.ncbi.nlm.nih.gov/pubmed/19065810 http://dx.doi.org/10.1007/978-1-60327-101-1_9 |
work_keys_str_mv | AT yangzhengrong peptidebioinformaticspeptideclassificationusingpeptidemachines |