School of Computer Software, College of Intelligence and Computing, Tianjin University, China.
Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
Brief Bioinform. 2021 Jul 20;22(4). doi: 10.1093/bib/bbaa313.
The spatial distribution of proteome at subcellular levels provides clues for protein functions, thus is important to human biology and medicine. Imaging-based methods are one of the most important approaches for predicting protein subcellular location. Although deep neural networks have shown impressive performance in a number of imaging tasks, its application to protein subcellular localization has not been sufficiently explored. In this study, we developed a deep imaging-based approach to localize the proteins at subcellular levels. Based on deep image features extracted from convolutional neural networks (CNNs), both single-label and multi-label locations can be accurately predicted. Particularly, the multi-label prediction is quite a challenging task. Here we developed a criterion learning strategy to exploit the label-attribute relevancy and label-label relevancy. A criterion that was used to determine the final label set was automatically obtained during the learning procedure. We concluded an optimal CNN architecture that could give the best results. Besides, experiments show that compared with the hand-crafted features, the deep features present more accurate prediction with less features. The implementation for the proposed method is available at https://github.com/RanSuLab/ProteinSubcellularLocation.
亚细胞水平蛋白质组的空间分布为蛋白质功能提供了线索，因此对人类生物学和医学很重要。基于成像的方法是预测蛋白质亚细胞位置的最重要方法之一。尽管深度神经网络在许多成像任务中表现出了令人印象深刻的性能，但它在蛋白质亚细胞定位中的应用尚未得到充分探索。在这项研究中，我们开发了一种基于深度成像的方法来定位亚细胞水平的蛋白质。基于从卷积神经网络（CNN）中提取的深度图像特征，可以准确预测单标签和多标签位置。特别是，多标签预测是一项极具挑战性的任务。在这里，我们开发了一种准则学习策略来利用标签属性相关性和标签标签相关性。在学习过程中，自动获得用于确定最终标签集的准则。我们得出了一个最佳的 CNN 架构，可以给出最好的结果。此外，实验表明，与手工制作的特征相比，深度特征具有更少的特征，并且具有更准确的预测。该方法的实现可在 https://github.com/RanSuLab/ProteinSubcellularLocation 上获得。
