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.
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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.
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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
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DOI: https://doi.org/10.1007/978-3-319-42291-6_35
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