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The Scientific World Journal
Volume 2015, Article ID 945927, 7 pages
http://dx.doi.org/10.1155/2015/945927
Review Article

Briefing in Application of Machine Learning Methods in Ion Channel Prediction

1Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
2Department of Physics, School of Sciences and Center for Genomics and Computational Biology, Hebei United University, Tangshan 063000, China

Received 31 July 2014; Accepted 11 September 2014

Academic Editor: Ramu Anandakrishnan

Copyright © 2015 Hao Lin and Wei Chen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. B. Hille, C. M. Armstrong, and R. MacKinnon, “Ion channels: from idea to reality,” Nature Medicine, vol. 5, no. 10, pp. 1105–1109, 1999. View at Publisher · View at Google Scholar · View at Scopus
  2. D. C. Camerino, J.-F. Desaphy, D. Tricarico, S. Pierno, and A. Liantonio, “Therapeutic approaches to ion channel diseases,” Advances in Genetics, vol. 64, pp. 81–145, 2008. View at Publisher · View at Google Scholar · View at Scopus
  3. R. S. Kass, “The channelopathies: novel insights into molecular and genetic mechanisms of human disease,” The Journal of Clinical Investigation, vol. 115, no. 8, pp. 1986–1989, 2005. View at Publisher · View at Google Scholar · View at Scopus
  4. J. J. Clare, “Targeting ion channels for drug discovery,” Discovery Medicine, vol. 9, no. 46, pp. 253–260, 2010. View at Google Scholar · View at Scopus
  5. I. S. Gabashvili, B. H. A. Sokolowski, C. C. Morton, and A. B. S. Giersch, “Ion channel gene expression in the inner ear,” JARO: Journal of the Association for Research in Otolaryngology, vol. 8, no. 3, pp. 305–328, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. T. Dudev and C. Lim, “Determinants of K+ vs Na+ selectivity in potassium channels,” Journal of the American Chemical Society, vol. 131, no. 23, pp. 8092–8101, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. P. W. Rose, C. Bi, W. F. Bluhm et al., “The RCSB protein data bank: new resources for research and education,” Nucleic Acids Research, vol. 41, pp. D475–D482, 2013. View at Publisher · View at Google Scholar · View at Scopus
  8. UniProt Consortium, “Update on activities at the Universal Protein Resource (UniProt) in 2013,” Nucleic Acids Research, vol. 41, pp. D43–D47, 2013. View at Publisher · View at Google Scholar
  9. T. Kenakin, “New concepts in pharmacological efficacy at 7TM receptors: IUPHAR review 2,” The British Journal of Pharmacology, vol. 168, no. 3, pp. 554–575, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. M. Donizelli, M.-A. Djite, and N. le Novère, “LGICdb: a manually curated sequence database after the genomes,” Nucleic Acids Research, vol. 34, pp. D267–D269, 2006. View at Publisher · View at Google Scholar · View at Scopus
  11. W. J. Gallin and P. A. Boutet, “VKCDB: voltage-gated K+ channel database updated and upgraded,” Nucleic Acids Research, vol. 39, no. 1, pp. D362–D366, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. L. Fu, B. Niu, Z. Zhu, S. Wu, and W. Li, “CD-HIT: accelerated for clustering the next-generation sequencing data,” Bioinformatics, vol. 28, no. 23, pp. 3150–3152, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. G. Wang and R. L. Dunbrack Jr., “PISCES: a protein sequence culling server,” Bioinformatics, vol. 19, no. 12, pp. 1589–1591, 2003. View at Publisher · View at Google Scholar · View at Scopus
  14. W. Li, L. Fu, B. Niu, S. Wu, and J. Wooley, “Ultrafast clustering algorithms for metagenomic sequence analysis,” Briefings in Bioinformatics, vol. 13, no. 6, Article ID bbs035, pp. 656–668, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. W. Chen and H. Lin, “Identification of voltage-gated potassium channel subfamilies from sequence information using support vector machine,” Computers in Biology and Medicine, vol. 42, no. 4, pp. 504–507, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. H. Lin and H. Ding, “Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition,” Journal of Theoretical Biology, vol. 269, pp. 64–69, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  17. L.-X. Liu, M.-L. Li, F.-Y. Tan et al., “Local sequence information-based support vector machine to classify voltage-gated potassium channels,” Acta Biochimica et Biophysica Sinica, vol. 38, no. 6, pp. 363–371, 2006. View at Publisher · View at Google Scholar · View at Scopus
  18. W. X. Liu, E. Z. Deng, W. Chen, and H. Lin, “Identifying the subfamilies of voltage-gated potassium channels using feature selection technique,” International Journal of Molecular Sciences, vol. 15, pp. 12940–12951, 2014. View at Publisher · View at Google Scholar
  19. S. Saha, J. Zack, B. Singh, and G. P. S. Raghava, “VGIchan: prediction and classification of voltage-gated ion channels,” Genomics, Proteomics and Bioinformatics, vol. 4, no. 4, pp. 253–258, 2006. View at Publisher · View at Google Scholar · View at Scopus
  20. L. Zhu, J. Yang, J.-N. Song, K.-C. Chou, and H.-B. Shen, “Improving the accuracy of predicting disulfide connectivity by feature selection,” Journal of Computational Chemistry, vol. 31, no. 7, pp. 1478–1485, 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. C.-C. Chang and C.-J. Lin, “LIBSVM: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, article 27, 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. T. Joachims, Learning to classify text using support vector machines [M.S. thesis], Kluwer Academic Publishers, 2002.
  23. S.-H. Guo, E.-Z. Deng, L.-Q. Xu et al., “INuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition,” Bioinformatics, vol. 30, no. 11, pp. 1522–1529, 2014. View at Publisher · View at Google Scholar · View at Scopus
  24. K.-C. Chou, “Some remarks on protein attribute prediction and pseudo amino acid composition,” Journal of Theoretical Biology, vol. 273, pp. 236–247, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  25. M. R. Bakhtiarizadeh, M. Moradi-Shahrbabak, M. Ebrahimi, and E. Ebrahimie, “Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology,” Journal of Theoretical Biology, vol. 356, pp. 213–222, 2014. View at Publisher · View at Google Scholar · View at Scopus
  26. L. Lan, N. Djuric, Y. Guo, and S. Vucetic, “MS-kNN: protein function prediction by integrating multiple data sources,” BMC Bioinformatics, vol. 14, no. 3, article S8, 2013. View at Publisher · View at Google Scholar · View at Scopus
  27. Z.-H. You, Y.-K. Lei, L. Zhu, J. Xia, and B. Wang, “Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis,” BMC Bioinformatics, vol. 14, supplement 8, article S10, 2013. View at Publisher · View at Google Scholar · View at Scopus
  28. M. K. Leung, H. Y. Xiong, L. J. Lee, and B. J. Frey, “Deep learning of the tissue-regulated splicing code,” Bioinformatics, vol. 30, pp. i121–i129, 2014. View at Publisher · View at Google Scholar
  29. B.-Q. Li, L.-L. Hu, S. Niu, Y.-D. Cai, and K.-C. Chou, “Predict and analyze S-nitrosylation modification sites with the mRMR and IFS approaches,” Journal of Proteomics, vol. 75, no. 5, pp. 1654–1665, 2012. View at Publisher · View at Google Scholar · View at Scopus
  30. X. Li, H. Hu, and L. Shu, “Predicting human immunodeficiency virus protease cleavage sites in nonlinear projection space,” Molecular and Cellular Biochemistry, vol. 339, no. 1-2, pp. 127–133, 2010. View at Publisher · View at Google Scholar · View at Scopus
  31. S. Zhang, F. Ye, and X. Yuan, “Using principal component analysis and support vector machine to predict protein structural class for low-similarity sequences via PSSM,” Journal of Biomolecular Structure & Dynamics, vol. 29, no. 6, pp. 634–642, 2012. View at Google Scholar · View at Scopus
  32. H. T. Deng and G. Runger, “Feature selection via regularized trees,” in International Joint Conference on Neural Networks (IJCNN '12), IEEE, 2012.
  33. J. Zhang, X. Zhao, P. Sun, and Z. Ma, “PSNO: predicting cysteine S-nitrosylation sites by incorporating various sequence-derived features into the general form of Chou's PseAAC,” International Journal of Molecular Sciences, vol. 15, no. 7, pp. 11204–11219, 2014. View at Publisher · View at Google Scholar
  34. C. M. Livi and E. Blanzieri, “Protein-specific prediction of mRNA binding using RNA sequences, binding motifs and predicted secondary structures,” BMC Bioinformatics, vol. 15, no. 1, article 123, 2014. View at Publisher · View at Google Scholar · View at Scopus
  35. A. A. Adl, A. Nowzari-Dalini, B. Xue, V. N. Uversky, and X. Qian, “Accurate prediction of protein structural classes using functional domains and predicted secondary structure sequences,” Journal of Biomolecular Structure & Dynamics, vol. 29, no. 6, pp. 623–633, 2012. View at Publisher · View at Google Scholar · View at Scopus