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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...

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Detalles Bibliográficos
Autores principales: Li, Ying Hong, Xu, Jing Yu, Tao, Lin, Li, Xiao Feng, Li, Shuang, Zeng, Xian, Chen, Shang Ying, Zhang, Peng, Qin, Chu, Zhang, Cheng, Chen, Zhe, Zhu, Feng, Chen, Yu Zong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4985167/
https://www.ncbi.nlm.nih.gov/pubmed/27525735
http://dx.doi.org/10.1371/journal.pone.0155290
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author Li, Ying Hong
Xu, Jing Yu
Tao, Lin
Li, Xiao Feng
Li, Shuang
Zeng, Xian
Chen, Shang Ying
Zhang, Peng
Qin, Chu
Zhang, Cheng
Chen, Zhe
Zhu, Feng
Chen, Yu Zong
author_facet Li, Ying Hong
Xu, Jing Yu
Tao, Lin
Li, Xiao Feng
Li, Shuang
Zeng, Xian
Chen, Shang Ying
Zhang, Peng
Qin, Chu
Zhang, Cheng
Chen, Zhe
Zhu, Feng
Chen, Yu Zong
author_sort Li, Ying Hong
collection PubMed
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.
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spelling pubmed-49851672016-08-29 SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity Li, Ying Hong Xu, Jing Yu Tao, Lin Li, Xiao Feng Li, Shuang Zeng, Xian Chen, Shang Ying Zhang, Peng Qin, Chu Zhang, Cheng Chen, Zhe Zhu, Feng Chen, Yu Zong PLoS One Research Article 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. Public Library of Science 2016-08-15 /pmc/articles/PMC4985167/ /pubmed/27525735 http://dx.doi.org/10.1371/journal.pone.0155290 Text en © 2016 Li et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Li, Ying Hong
Xu, Jing Yu
Tao, Lin
Li, Xiao Feng
Li, Shuang
Zeng, Xian
Chen, Shang Ying
Zhang, Peng
Qin, Chu
Zhang, Cheng
Chen, Zhe
Zhu, Feng
Chen, Yu Zong
SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity
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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4985167/
https://www.ncbi.nlm.nih.gov/pubmed/27525735
http://dx.doi.org/10.1371/journal.pone.0155290
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