Prediction of Functional Class of Proteins and Peptides Irrespective of Sequence Homology by Support Vector Machines
Various computational methods have been used for the prediction of protein and peptide function based on their sequences. A particular challenge is to derive functional properties from sequences that show low or no homology to proteins of known function. Recently, a machine learning method, support...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2007-01-01
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| Series: | Bioinformatics and Biology Insights |
| Subjects: | |
| Online Access: | http://la-press.com/article.php?article_id=407 |
| _version_ | 1818346298896547840 |
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| author | Zhi Qun Tang Hong Huang Lin Hai Lei Zhang Lian Yi Han Xin Chen Yu Zong Chen |
| author_facet | Zhi Qun Tang Hong Huang Lin Hai Lei Zhang Lian Yi Han Xin Chen Yu Zong Chen |
| author_sort | Zhi Qun Tang |
| collection | DOAJ |
| description | Various computational methods have been used for the prediction of protein and peptide function based on their sequences. A particular challenge is to derive functional properties from sequences that show low or no homology to proteins of known function. Recently, a machine learning method, support vector machines (SVM), have been explored for predicting functional class of proteins and peptides from amino acid sequence derived properties independent of sequence similarity, which have shown promising potential for a wide spectrum of protein and peptide classes including some of the low- and non-homologous proteins. This method can thus be explored as a potential tool to complement alignment-based, clusteringbased, and structure-based methods for predicting protein function. This article reviews the strategies, current progresses, and underlying diffi culties in using SVM for predicting the functional class of proteins. The relevant software and web-servers are described. The reported prediction performances in the application of these methods are also presented. |
| first_indexed | 2024-12-13T17:16:03Z |
| format | Article |
| id | doaj.art-c4942191d0ee42a59dbcfa94dbfa85ec |
| institution | Directory Open Access Journal |
| issn | 1177-9322 |
| language | English |
| last_indexed | 2024-12-13T17:16:03Z |
| publishDate | 2007-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Bioinformatics and Biology Insights |
| spelling | doaj.art-c4942191d0ee42a59dbcfa94dbfa85ec2022-12-21T23:37:25ZengSAGE PublishingBioinformatics and Biology Insights1177-93222007-01-0111947Prediction of Functional Class of Proteins and Peptides Irrespective of Sequence Homology by Support Vector MachinesZhi Qun TangHong Huang LinHai Lei ZhangLian Yi HanXin ChenYu Zong ChenVarious computational methods have been used for the prediction of protein and peptide function based on their sequences. A particular challenge is to derive functional properties from sequences that show low or no homology to proteins of known function. Recently, a machine learning method, support vector machines (SVM), have been explored for predicting functional class of proteins and peptides from amino acid sequence derived properties independent of sequence similarity, which have shown promising potential for a wide spectrum of protein and peptide classes including some of the low- and non-homologous proteins. This method can thus be explored as a potential tool to complement alignment-based, clusteringbased, and structure-based methods for predicting protein function. This article reviews the strategies, current progresses, and underlying diffi culties in using SVM for predicting the functional class of proteins. The relevant software and web-servers are described. The reported prediction performances in the application of these methods are also presented.http://la-press.com/article.php?article_id=407machine learning methodpeptide functionprotein familyprotein functionprotein function predictionsupport vector machines |
| spellingShingle | Zhi Qun Tang Hong Huang Lin Hai Lei Zhang Lian Yi Han Xin Chen Yu Zong Chen Prediction of Functional Class of Proteins and Peptides Irrespective of Sequence Homology by Support Vector Machines Bioinformatics and Biology Insights machine learning method peptide function protein family protein function protein function prediction support vector machines |
| title | Prediction of Functional Class of Proteins and Peptides Irrespective of Sequence Homology by Support Vector Machines |
| title_full | Prediction of Functional Class of Proteins and Peptides Irrespective of Sequence Homology by Support Vector Machines |
| title_fullStr | Prediction of Functional Class of Proteins and Peptides Irrespective of Sequence Homology by Support Vector Machines |
| title_full_unstemmed | Prediction of Functional Class of Proteins and Peptides Irrespective of Sequence Homology by Support Vector Machines |
| title_short | Prediction of Functional Class of Proteins and Peptides Irrespective of Sequence Homology by Support Vector Machines |
| title_sort | prediction of functional class of proteins and peptides irrespective of sequence homology by support vector machines |
| topic | machine learning method peptide function protein family protein function protein function prediction support vector machines |
| url | http://la-press.com/article.php?article_id=407 |
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