Multi-Site Protein Subcellular Localization Based on Deep Convolutional Neural Network

Hanhan Cong, Hong Liu, Yuehui Chen, Ya-ou Zhao, Lei Wang
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Abstract

Each part of internal structure of cells which is commonly mentioned as subcellular is highly ordered and interconnected has unique functions. The experiments show that deviated protein delivery to the corresponding subcellular causes of human disease. Studies of protein localization can clarify pathogenesis and find treatments. As protein subcellular localization has a very important position in the field of biology, the research in this area is extremely active. Most of the existing protein sub cellular localization methods are more suitable for single-site sub cellular localization. This paper proposed an algorithm based deep convolution neural network which is suit for multi-site protein subcellular localization and the algorithm is implemented on the human protein database to verify and analyze the performance. In order to further improve the classification result of the algorithm, it was combined ensemble learning and features fusion. It can be inferred from experiments that the proposed algorithm is effective in multi-site protein subcellular localization and the overall correct rate of classification is 59.13% which is higher than SAE, SVM and RF. The algorithm proposed in this paper is more uniform and less affected by the number of samples. When the data samples are different, the classification results will have a certain impact, but the overall classification is good. Besides ensemble learning and features fusion are effective for improving classification result.
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基于深度卷积神经网络的蛋白质多位点亚细胞定位
细胞内部结构的每一部分,通常被称为亚细胞,都是高度有序和相互连接的,具有独特的功能。实验表明,偏离蛋白质输送到相应的亚细胞是引起人类疾病的原因。研究蛋白质定位可以阐明发病机制并找到治疗方法。由于蛋白质亚细胞定位在生物学领域具有非常重要的地位,该领域的研究非常活跃。现有的蛋白质亚细胞定位方法大多更适合于单位点亚细胞定位。本文提出了一种适用于多位点蛋白质亚细胞定位的基于深度卷积神经网络的算法,并将该算法在人类蛋白质数据库中实现,以验证和分析其性能。为了进一步提高算法的分类效果,将集成学习与特征融合相结合。实验表明,该算法在蛋白质亚细胞多位点定位中是有效的,分类的总体正确率为59.13%,高于SAE、SVM和RF。本文提出的算法更加均匀,受样本数量的影响较小。当数据样本不同时,分类结果会有一定的影响,但总体分类是好的。此外,集成学习和特征融合对于提高分类结果也是有效的。
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