About: 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.

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About: 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. Goto Sponge NotDistinct Permalink

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value	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.
subject	Bioinformatics Peptides Protein structure Molecular biology Stereochemistry
part of	Peptide Bioinformatics- Peptide Classification Using Peptide Machines
is abstract of	Peptide Bioinformatics- Peptide Classification Using Peptide Machines
is hasSource of	covid:ann/target/e8c9303d69dc1755998543f79ca557a70b25ec0a covid:ann/target/b4fc363b969c2a44c9f8ff84bc6ee00d7c904688 covid:ann/target/e8847567cef024d94dbb3d076a83bf996d743369 covid:ann/target/ee05dae7f854c5c3c31bc45ff411f0bc6d2a6549 covid:ann/target/217a3a46e82ca43aa3dcd0991491907ea54270c2 »more»

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