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《Journal of Hubei University for Nationalities(Natural Science Edition)》 2018-01
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Predicting of Subcellular Location of Human Protein Based on Multi-label Learning

ZHAI Yunqing;ZHAI Shengnan;School of Computer and Communication Engineering,Zhengzhou University of Light Industry;  
Cells are considered to be the smallest units of biological function,while proteins are biological macromolecules that make up cells,and play a crucial role in the biological activities of life.Given a protein sequence,we can predict which specific organelles work,such as cell membranes,mitochondria,etc.,and this method is called protein subcellular localization.Predicting protein subcellular localization is a necessary step in understanding its function and determining drug targets.As the existing prediction methods can only predict the subcellular location of individual proteins,this paper focuses on predicting protein subcellular location based on multi-label learning,containing 3 077 apoptosis protein data set based on the GO features extraction and prediction using the LIFT_ PCC algorithm.The experimental results show that the overall accuracy of the proposed method reaches 59.36%,and the performance test is satisfying.The proposed method will become a very useful high-throughput tool.
【Fund】: 国家自然科学基金项目(61402422)
【CateGory Index】: R3411
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