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SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity

Author

Listed:
  • 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

Abstract

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.

Suggested Citation

  • 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, 2016. "SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-14, August.
  • Handle: RePEc:plo:pone00:0155290
    DOI: 10.1371/journal.pone.0155290
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    References listed on IDEAS

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    1. Sheila M Reynolds & Lukas Käll & Michael E Riffle & Jeff A Bilmes & William Stafford Noble, 2008. "Transmembrane Topology and Signal Peptide Prediction Using Dynamic Bayesian Networks," PLOS Computational Biology, Public Library of Science, vol. 4(11), pages 1-14, November.
    2. Chen Lin & Ying Zou & Ji Qin & Xiangrong Liu & Yi Jiang & Caihuan Ke & Quan Zou, 2013. "Hierarchical Classification of Protein Folds Using a Novel Ensemble Classifier," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-11, February.
    3. Stephen P. Jackson & Jiri Bartek, 2009. "The DNA-damage response in human biology and disease," Nature, Nature, vol. 461(7267), pages 1071-1078, October.
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    Cited by:

    1. Solomon Shiferaw Beyene & Tianyi Ling & Blagoj Ristevski & Ming Chen, 2020. "A novel riboswitch classification based on imbalanced sequences achieved by machine learning," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-23, July.

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