OUP accepted manuscript
OUP accepted manuscript
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DOI：
10.1093/bib/bbab605
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发表时间：
2021
影响因子：
9.5
通讯作者：
中科院分区：
文献类型：
--
作者：
随着高分辨率显微成像数据的快速增长，揭示人类蛋白质的亚细胞图谱已成为空间蛋白质组研究的中心任务。人类蛋白质图谱（HPA）的细胞图谱为在细胞水平上识别亚细胞定位模式提供了宝贵的资源，大规模注释数据可以通过先进的深度神经网络进行学习。然而，现有的预测器仍然受到不平衡的类分布和缺乏标记的数据的小类。因此，有必要制定新的方法来应对这些问题。我们利用自我监督学习协议来解决这些问题。特别是，我们提出了一个预训练计划，以加强传统的监督学习框架称为SIFLoc。预训练的特点是混合数据增强方法和修改的对比损失函数，旨在从显微图像中学习良好的特征表示。实验是在从HPA数据库收集的大规模免疫荧光显微图像数据集上进行的。使用与分类器相同的深度神经网络，通过SIFLoc预训练的模型不仅比没有预训练的模型表现更好，而且比最先进的自监督学习方法更具优势。特别是，SIFLoc显著提高了对次要细胞器的预测精度。
With the rapid growth of high-resolution microscopy imaging data, revealing the subcellular map of human proteins has become a central task in the spatial proteome. The cell atlas of the Human Protein Atlas (HPA) provides precious resources for recognizing subcellular localization patterns at the cell level, and the large-scale annotated data enable learning via advanced deep neural networks. However, the existing predictors still suffer from the imbalanced class distribution and the lack of labeled data for minor classes. Thus, it is necessary to develop new methods for coping with these issues. We leverage the self-supervised learning protocol to address these problems. Especially, we propose a pre-training scheme to enhance the conventional supervised learning framework called SIFLoc. The pre-training is featured by a hybrid data augmentation method and a modified contrastive loss function, aiming to learn good feature representations from microscopic images. The experiments are performed on a large-scale immunofluorescence microscopic image dataset collected from the HPA database. Using the same deep neural networks as the classifier, the model pre-trained via SIFLoc not only outperforms the model without pre-training by a large margin but also shows advantages over the state-of-the-art self-supervised learning methods. Especially, SIFLoc improves the prediction accuracy for minor organelles significantly.