Abstract
Accurate classification on protein subcellular localization plays an important role in Bioinformatics. An increasingly evidences demonstrate that a variety of classification methods have been employed in this field. This research adopts feature fusion method to extract the information of the protein subcellular. Several types of features are employed in this protein coding method, which include amino acid index distribution, the stereo-chemical properties of amino acids and the information for local sequence of amino acids. On base of this feature combination method, flexible neutral tree (FNT) is employed to predict multiplex protein subcellular locations. The overall accuracy rate of using flexible neutral tree as prediction algorithm may reach a better result.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Du, P.F., Xu, C.: Predicting multisite protein subcellular locations: progress and challenges. Expert Rev. Proteomics 10, 227–237 (2013)
Chou, K.C.: Some remarks on predicting multi-label attributes in molecular biosystems. Mol. BioSyst. 9, 1092–1100 (2013)
Xiao, X., Wu, Z.C., Chou, K.C.: iLoc-Virus: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites. J. Theor. Biol. 284, 42–51 (2011)
Chou, K.C.: Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Curr. Proteomics 6, 262–274 (2009)
Zhang, M.L., Zhou, Z.H.: ML_KNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40, 2038–2048 (2007)
Wan, S., Mak, M., Kung, S.: mGOASVM: multi-label protein subcellular localization based on gene ontology and support vector machines. BMC Bioinform. 13(1), 290 (2012)
Su, C.Y., Lo, A., Lin, C.C., et al.: A novel approach for prediction of multi-labeled protein subcellular localization for prokaryotic bacteria. In: Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference Workshops, pp. 79–80. IEEE, Stanford, California, Piscataway, 8–12 August 2005
Shen, H.B., Chou, K.C.: Gpos-mPLoc: a top-down approach to improve the quality of predicting subcellular localization of gram-positive bacterial proteins. Protein Pept. Lett. 16, 1478–1484 (2009)
Meis, A., Andradenavarro, M.: A novel approach for protein subcellular location prediction using amino acid exposure. BMC Bioinform. 14, 342 (2013)
Luo, H.: Predicted protein subcellular localization in dominant surface ocean bacterioplankton. Appl. Environ. Microbiol. 78(18), 6550–6557 (2012)
Mooney, C., Wang, Y., Pollastri, G.: SCLpred: protein subcellular localization prediction by N-to-1 neural networks. Bioinformatics 27(20), 2812–2819 (2011)
Yu, H., Jiang, W., Liu, Q.: Expression pattern and subcellular localization of the ovate protein family in rice. PLoS ONE 10(3), e0118966 (2015)
Wu, Z., Xiao, X., Chou, K.: iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites. Mol. BioSyst. 7(12), 3287–3297 (2011)
Yang, B., Chen, Y.H., Jiang, M.Y.: Reverse engineering of gene regulatory networks using flexible neural tree models. Neurocomputing 99, 458–466 (2013)
Chen, Y.H., Yang, B., Dong, J.: Evolving flexible neural networks using ant programming and PSO algorithm. In: Yin, F.-L., Wang, J., Guo, C. (eds.) Advances in Neural Networks–ISNN. LNCS, vol. 3173, pp. 211–216. Springer, Heidelberg (2004)
Bao, W.Z., Chen, Y.H., Wang, D.: Prediction of protein structure classes with flexible neural tree. Bio-Med. Mater. Eng. 24, 3797–3806 (2014)
Reyck, B.D., Degraeve, Z., Vandenborre, R.: Project options valuation with net present value and decision tree analysis. Eur. J. Oper. Res. 184(1), 341–355 (2008)
Hanigovszki, N., Poulsen, J., Blaabjerg, F.: A novel output filter topology to reduce motor overvoltage. J. Electroanal. Chem. 40(3), 845–852 (2003)
Hwang, D., Green, P.: Bayesian Markov chain Monte Carlo sequence analysis reveals varying neutral substitution patterns in mammalian evolution. Proc. Natl. Acad. Sci. U.S.A. 101(39), 13994–14001 (2004)
Acknowledgment
This research was partially supported by the Youth Project of National Natural Science Fund (Grant No. 61302128), Shandong Provincial Natural Science Foundation, China, under Grant ZR2015JL025, the Youth Science and Technology Star Program of Jinan City (201406003), the Natural Science Foundation of Shandong Province (ZR2011FL022, ZR2013FL002), the Scientific Research Fund of Jinan University (XKY1410, XKY1411), the Program for Scientific research innovation team in Colleges and Universities of Shandong Province (2012–2015), and the Shandong Provincial Key Laboratory of Network Based Intelligent Computing.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, D., Bao, W., Chen, Y., He, W., Wang, L., Fan, Y. (2016). Predicting Subcellular Localization of Multiple Sites Proteins. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9771. Springer, Cham. https://doi.org/10.1007/978-3-319-42291-6_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-42291-6_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42290-9
Online ISBN: 978-3-319-42291-6
eBook Packages: Computer ScienceComputer Science (R0)