Analysis of protein subcellular localization is a critical part of proteomics. In recent years, as both the number and quality of microscopic images are increasing rapidly, many automated methods, especially convolutional neural networks (CNN), have been developed to predict protein subcellular location(s) based on bioimages, but their performance always suffers from some inherent properties of the problem. First, many microscopic images have non-informative or noisy sections, like unstained stroma and unspecific background, which affect the extraction of protein expression information. Second, the patterns of protein subcellular localization are very complex, as a lot of proteins locate in more than one compartment. In this study, we propose a new label-correlation enhanced deep neural network, laceDNN, to classify the subcellular locations of multi-label proteins from immunohistochemistry images. The model uses small representative patches as input to alleviate the image noise issue, and its backbone is a hybrid architecture of CNN and recurrent neural network, where the former network extracts representative image features and the latter learns the organelle dependency relationships. Our experimental results indicate that the proposed model can improve the performance of multi-label protein subcellular classification.

中文翻译：

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将标签相关性结合到深度神经网络中，对免疫组织化学图像中的蛋白质亚细胞定位模式进行分类

蛋白质亚细胞定位分析是蛋白质组学的关键部分。近年来，随着显微图像数量和质量的快速增长，许多自动化方法，尤其是卷积神经网络（CNN）被开发出来，用于基于生物图像预测蛋白质亚细胞位置，但它们的性能一直受到问题的一些固有属性。首先，许多显微图像具有非信息性或噪声部分，如未染色的基质和非特异性背景，这会影响蛋白质表达信息的提取。其次，蛋白质亚细胞定位的模式非常复杂，因为许多蛋白质位于多个隔室中。在这项研究中，我们提出了一种新的标签相关性增强深度神经网络 laceDNN，从免疫组织化学图像中对多标记蛋白的亚细胞位置进行分类。该模型使用小的代表性块作为输入来缓解图像噪声问题，其主干是 CNN 和循环神经网络的混合架构，前者网络提取代表性图像特征，后者学习细胞器依赖关系。我们的实验结果表明，所提出的模型可以提高多标签蛋白质亚细胞分类的性能。前者网络提取具有代表性的图像特征，后者学习细胞器依赖关系。我们的实验结果表明，所提出的模型可以提高多标签蛋白质亚细胞分类的性能。前者网络提取具有代表性的图像特征，后者学习细胞器依赖关系。我们的实验结果表明，所提出的模型可以提高多标签蛋白质亚细胞分类的性能。