SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity.
Knowledge of protein function is important for biological, medical and therapeutic studies, but many proteins are still unknown in function. There is a need for more improved functional prediction methods. Our SVM-Prot web-server employed a machine learning method for predicting protein functional f...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2016-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4985167?pdf=render |
| _version_ | 1819094885403197440 |
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| author | Ying Hong Li Jing Yu Xu Lin Tao Xiao Feng Li Shuang Li Xian Zeng Shang Ying Chen Peng Zhang Chu Qin Cheng Zhang Zhe Chen Feng Zhu Yu Zong Chen |
| author_facet | Ying Hong Li Jing Yu Xu Lin Tao Xiao Feng Li Shuang Li Xian Zeng Shang Ying Chen Peng Zhang Chu Qin Cheng Zhang Zhe Chen Feng Zhu Yu Zong Chen |
| author_sort | Ying Hong Li |
| collection | DOAJ |
| description | Knowledge of protein function is important for biological, medical and therapeutic studies, but many proteins are still unknown in function. There is a need for more improved functional prediction methods. Our SVM-Prot web-server employed a machine learning method for predicting protein functional families from protein sequences irrespective of similarity, which complemented those similarity-based and other methods in predicting diverse classes of proteins including the distantly-related proteins and homologous proteins of different functions. Since its publication in 2003, we made major improvements to SVM-Prot with (1) expanded coverage from 54 to 192 functional families, (2) more diverse protein descriptors protein representation, (3) improved predictive performances due to the use of more enriched training datasets and more variety of protein descriptors, (4) newly integrated BLAST analysis option for assessing proteins in the SVM-Prot predicted functional families that were similar in sequence to a query protein, and (5) newly added batch submission option for supporting the classification of multiple proteins. Moreover, 2 more machine learning approaches, K nearest neighbor and probabilistic neural networks, were added for facilitating collective assessment of protein functions by multiple methods. SVM-Prot can be accessed at http://bidd2.nus.edu.sg/cgi-bin/svmprot/svmprot.cgi. |
| first_indexed | 2024-12-21T23:34:31Z |
| format | Article |
| id | doaj.art-c4a2d1f6eb3143c8bf602ed365a8e53a |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-21T23:34:31Z |
| publishDate | 2016-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-c4a2d1f6eb3143c8bf602ed365a8e53a2022-12-21T18:46:25ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-01118e015529010.1371/journal.pone.0155290SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity.Ying Hong LiJing Yu XuLin TaoXiao Feng LiShuang LiXian ZengShang Ying ChenPeng ZhangChu QinCheng ZhangZhe ChenFeng ZhuYu Zong ChenKnowledge of protein function is important for biological, medical and therapeutic studies, but many proteins are still unknown in function. There is a need for more improved functional prediction methods. Our SVM-Prot web-server employed a machine learning method for predicting protein functional families from protein sequences irrespective of similarity, which complemented those similarity-based and other methods in predicting diverse classes of proteins including the distantly-related proteins and homologous proteins of different functions. Since its publication in 2003, we made major improvements to SVM-Prot with (1) expanded coverage from 54 to 192 functional families, (2) more diverse protein descriptors protein representation, (3) improved predictive performances due to the use of more enriched training datasets and more variety of protein descriptors, (4) newly integrated BLAST analysis option for assessing proteins in the SVM-Prot predicted functional families that were similar in sequence to a query protein, and (5) newly added batch submission option for supporting the classification of multiple proteins. Moreover, 2 more machine learning approaches, K nearest neighbor and probabilistic neural networks, were added for facilitating collective assessment of protein functions by multiple methods. SVM-Prot can be accessed at http://bidd2.nus.edu.sg/cgi-bin/svmprot/svmprot.cgi.http://europepmc.org/articles/PMC4985167?pdf=render |
| spellingShingle | Ying Hong Li Jing Yu Xu Lin Tao Xiao Feng Li Shuang Li Xian Zeng Shang Ying Chen Peng Zhang Chu Qin Cheng Zhang Zhe Chen Feng Zhu Yu Zong Chen SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity. PLoS ONE |
| title | SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity. |
| title_full | SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity. |
| title_fullStr | SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity. |
| title_full_unstemmed | SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity. |
| title_short | SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity. |
| title_sort | svm prot 2016 a web server for machine learning prediction of protein functional families from sequence irrespective of similarity |
| url | http://europepmc.org/articles/PMC4985167?pdf=render |
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