Abstract
The prediction of subcellular localisation of proteins is one of the main goals of proteome sequencing, and researchers have achieved high classification accuracy with the help of computer technology, but most of the current classification models are not applicable to the classification of plant vacuole proteins, and it is tedious and time-consuming to classify plant vacuole proteins using subcellular localisation methods. In this paper, we focus on the classification of plant vacuole proteins based on an ensemble stacking model. New feature inputs are generated by fusing statistical and physicochemical features of proteins. The data is accurately classified by using an ensemble stacking model based on a number of machine learning algorithms. The results show that the model achieves a classification accuracy of 73%, which is a significant advance compared to other models and is of high significance for studying the classification of plant vacuole proteins.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant No. 61902337), Xuzhou Science and Technology Plan Project (KC21047), Jiangsu Provincial Natural Science Foundation (No. SBK2019040953), Natural Science Fund for Colleges and Universities in Jiangsu Province (No. 19KJB520016) and Young Talents of Science and Technology in Jiangsu and ghfund202302026465.
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Ju, X., Xiao, K., He, L., Wang, Q., Wang, Z., Bao, W. (2023). Plant Vacuole Protein Classification with Ensemble Stacking Model. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14088. Springer, Singapore. https://doi.org/10.1007/978-981-99-4749-2_53
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DOI: https://doi.org/10.1007/978-981-99-4749-2_53
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