Prediction of protein subcellular location has currently become a hot topic because it has been proven to be useful for understanding both the disease mechanisms and novel drug design. With the rapid development of automated microscopic imaging technology in recent years, classification methods of bioimage-based protein subcellular location have attracted considerable attention for images can describe the protein distribution intuitively and in detail. In the current study, a prediction method of protein subcellular location was proposed based on multi-view image features that are extracted from three different views, including the four texture features of the original image, the global and local features of the protein extracted from the protein channel images after color segmentation, and the global features of DNA extracted from the DNA channel image. Finally, the extracted features were combined together to improve the performance of subcellular localization prediction. From the performance comparison of different combination features under the same classifier, the best ensemble features could be obtained. In this work, a classifier based on Stacked Auto-encoders and the random forest was also put forward. To improve the prediction results, the deep network was combined with the traditional statistical classification methods. Stringent cross-validation and independent validation tests on the benchmark dataset demonstrated the efficacy of the proposed method.

中文翻译：

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基于生物图像的蛋白质亚细胞位置预测，具有集成特征和深度网络


蛋白质亚细胞位置的预测目前已成为一个热门话题，因为它已被证明可用于理解疾病机制和新型药物设计。近年来，随着自动化显微成像技术的飞速发展，基于生物影像的蛋白质亚细胞定位分类方法因其图像能够直观、详细地描述蛋白质分布而受到广泛关注。在本研究中，提出了一种基于从 3 个不同视图提取的多视图图像特征的蛋白质亚细胞定位预测方法，包括原始图像的 4 个纹理特征、颜色分割后从蛋白质通道图像中提取的蛋白质的全局和局部特征，以及从 DNA 通道图像中提取的 DNA 的全局特征。最后，将提取的特征组合在一起，以提高亚细胞定位预测的性能。通过对同一分类器下不同组合特征的性能比较，可以得到最佳的融合特征。在这项工作中，还提出了一种基于堆叠自动编码器和随机森林的分类器。为了改进预测结果，将深度网络与传统的统计分类方法相结合。对基准数据集进行严格的交叉验证和独立验证测试证明了所提出的方法的有效性。