Plant and Animal sub cellular component localization prediction using multiple combination of various machine learning approaches

  • Abstract
  • Keywords
  • References
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  • Abstract

    Membrane proteins are encoded in the genome and functionally important in the living organisms. Information on subcellular localization of cellular proteins has a significant role in the function of cell organelles. Discovery of drug target and system biology between localization and biological function are highly correlated. Therefore, we are predicting the localization of protein using various machine learning approaches. The prediction system based on the integration of the outcome of five sequence based sub-classifiers. The subcellular localization prediction of the final result is based on protein profile vector, which is a result of the sub-classifiers.

  • Keywords

    Amino Acid Composition (AAC); Sub Cellular Localization; Gene Ontology (GO

  • References

      [1] Al-Mubaid, H., & Nguyen, D. B. (2014, November). New Feature Weighting Technique for Predicting Protein Subcellular Localization. In Bioinformatics and Bioengineering (BIBE), 2014 IEEE International Conference on (pp. 163-167). IEEE.

      [2] Chou, K. C., & Shen, H. B. (2010). A new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mPLoc 2.0. PLoS One, 5(4), e9931.

      [3] Yang, W. Y., Lu, B. L., & Kwok, J. T. (2011). Incorporating cellular sorting structure for better prediction of protein subcellular locations. Journal of Experimental & Theoretical Artificial Intelligence, 23(1), 79-95.

      [4] Horton, P., Park, K. J., Obayashi, T., &Nakai, K. (2006, February). Protein Subcellular Localisation Prediction with WoLF PSORT. In APBC (Vol. 39).

      [5] Chou, K. C., & Shen, H. B. (2010). Plant-mPLoc: a top-down strategy to augment the power for predicting plant protein subcellular localization. PloS one, 5(6), e11335. E. P. Wigner, “Theory of traveling-wave optical laser,”Phys .Rev. vol. 134, pp. A635–A646, Dec. 1965.

      [6] Hsiao, H. C., Chen, S. H., Chang, J. P. C., & Tsai, J. J. (2008). Predicting Subcellular Locations of Eukaryotic Proteins Using Bayesian and k-Nearest Neighbor Classifiers. Journal of Information Science & Engineering, 24(5).

      [7] Song, C., & Shi, F. (2010). Prediction of Subcellular Localization of Apoptosis Proteins by Dipeptide Composition. JDCTA, 4(1), 32-36.

      [8] Wan, S., Mak, M. W., & Kung, S. Y. (2011, September). Protein subcellular localization prediction based on profile alignment and Gene Ontology. In Machine Learning for Signal Processing (MLSP), 2011 IEEE International Workshop on (pp. 1-6). IEEE.

      [9] Chou, K. C., & Elrod, D. W. (1999). Protein subcellular location prediction. Protein engineering, 12(2), 107-118.

      [10] Xu, Q., Pan, S. J., Xue, H. H., & Yang, Q. (2011). Multitask learning for protein subcellular location prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 8(3), 748-759.

      [11] Yu, D., Wu, X., Shen, H., Yang, J., Tang, Z., Qi, Y., & Yang, J. (2012). Enhancing membrane protein subcellular localization prediction by parallel fusion of multi-view features. IEEE transactions on nan bioscience, 11(4), 375-385

      [12] Li, F., & Zhou, H. (2011, October). Predicting protein subcellular location based on improved quadratic discriminant. In Biomedical Engineering and Informatics (BMEI), 2011 4th International Conference on (Vol. 4, pp. 1989-1992). IEEE.

      [13] Xie, D., Li, A., Lin, X., Wang, M., Jiang, Z., & Feng, H. (2006, January). Using motifs in the prediction of eukaryotic protein subcellular localization. In Engineering in Medicine and Biology Society, 2005. IEEEEMBS 2005. 27th Annual International Conference of the (pp. 2802-2804). IEEE.

      [14] Ogul, H., &Mumcuoglu, E. U. (2007). Subcellular localization prediction with new protein encoding schemes. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 4(2).

      [15] Ogul, H., &Mumcuoglu, E. U. (2007). Subcellular localization prediction with new protein encoding schemes. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 4(2).

      [16] Juan, E. Y., Chang, J. H., Li, C. H., & Chen, B. Y. (2011, June). Methods for Protein Subcellular Localization Prediction. In Complex, Intelligent and Software Intensive Systems (CISIS), 2011 International

      [17] Bipin Nair B J, Pranav V, AthulyaViswan.(2017) Comparative sequence analysis and 3D structure prediction of various stages of lymphoma using a combined approach of pairwise and Needleman Wunsch (ICIIECS)

      [18] Okou, D. T., Locke, A. E., Steinberg, K. M., Hagen, K., Athri, P., Shetty, A. C., & Zwick, M. E. (2009). Combining Microarray‐based Genomic Selection (MGS) with the Illumina Genome Analyzer Platform to Sequence Diploid Target Regions. Annals of human genetics, 73(5), 502-513.

      [19] Qu, X., Chen, Y., Qiao, S., Wang, D., & Zhao, Q. (2014, August). Predicting the subcellular localization of proteins with multiple sites based on multiple features fusion. In International Conference on Intelligent Computing (pp. 456-465). Springer, Cham.

      [20] Peng, X., Wang, J., Zhong, J., Luo, J., & Pan, Y. (2015, November). An efficient method to identify essential proteins for different species by integrating protein subcellular localization information. In Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on (pp. 277-280). IEEE.




Article ID: 9828
DOI: 10.14419/ijet.v7i1.9.9828

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