In the present paper, deep convolutional neural network (DCNN) is applied to multilocus protein subcellular localization as it is more suitable for multi-class classification. There are two main problems with this application. First, the appropriate features for correlation between multiple sites are hard to find. Second, the classifier structure is difficult to determine as it is greatly affected by the distribution of classified data. To solve these problems, a self-evoluting framework using DCNNs for multilocus protein subcellular localization is proposed. It has three characteristics that the previous algorithms do not. The first is that it combines the ant colony algorithm with the DCNN to form a self-evoluting algorithm for multilocus protein subcellular localization. The second is that it randomly groups subcellular sites using a limited random k-labelsets multi-label classification method. It also solves complex problems in a divide-and-conquer approach and proposes a flexible expansion model. The third is that it realizes the random selection feature extraction method in the positioning process and avoids the defects in individual feature extraction methods. The algorithm in the present paper is tested on the human database, and the overall correct rate is 67.17%, which is higher than that for the stacked self-encoder (SAE), support vector machine (SVM), random forest classifier (RF), or single deep convolutional neural network.

Graphical abstract

在本文中，深度卷积神经网络（DCNN）被应用于多位点蛋白质亚细胞定位，因为它更适合多类分类。这个应用程序有两个主要问题。首先，很难找到用于多个站点之间相关性的适当特征。其次，分类器结构难以确定，因为它受分类数据分布的影响很大。为了解决这些问题，提出了一种使用 DCNN 进行多位点蛋白质亚细胞定位的自我进化框架。它具有以前算法所没有的三个特征。首先是将蚁群算法与DCNN相结合，形成多位点蛋白质亚细胞定位的自进化算法。第二个是它使用有限的随机 k-labelsets 多标签分类方法对亚细胞位点进行随机分组。它还以分而治之的方式解决复杂问题，并提出了灵活的扩展模型。三是在定位过程中实现了随机选择特征提取方法，避免了个别特征提取方法的缺陷。本文算法在人体数据库上进行测试，整体正确率为67.17%，高于堆叠自编码器（SAE）、支持向量机（SVM）、随机森林分类器（RF） ，或单个深度卷积神经网络。三是在定位过程中实现了随机选择特征提取方法，避免了个别特征提取方法的缺陷。本文算法在人体数据库上进行测试，整体正确率为67.17%，高于堆叠自编码器（SAE）、支持向量机（SVM）、随机森林分类器（RF） ，或单个深度卷积神经网络。三是在定位过程中实现了随机选择特征提取方法，避免了个别特征提取方法的缺陷。本文算法在人体数据库上进行测试，整体正确率为67.17%，高于堆叠自编码器（SAE）、支持向量机（SVM）、随机森林分类器（RF） ，或单个深度卷积神经网络。

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