The identification of blood-secretory proteins and the detection of protein biomarkers in the blood have an important clinical application value. Existing methods for predicting blood-secretory proteins are mainly based on traditional machine learning algorithms, and heavily rely on annotated protein features. Unlike traditional machine learning algorithms, deep learning algorithms can automatically learn better feature representations from raw data, and are expected to be more promising to predict blood-secretory proteins. We present a novel deep learning model (DeepHBSP) combined with transfer learning by integrating a binary classification network and a ranking network to identify blood-secretory proteins from the amino acid sequence information alone. The loss function of DeepHBSP in the training step is designed to apply descriptive loss and compactness loss to the binary classification network and the ranking network, respectively. The feature extraction subnetwork of DeepHBSP is composed of a multi-lane capsule network. Additionally, transfer learning is used to train a highly accurate generalized model with small samples of blood-secretory proteins. The main contributions of this study are as follows: 1) a novel deep learning architecture by integrating a binary classification network and a ranking network is proposed, superior to existing traditional machine learning algorithms and other state-of-the-art deep learning architectures for biological sequence analysis; 2) the proposed model for blood-secretory protein prediction uses only amino acid sequences, overcoming the heavy dependence of existing methods on annotated protein features; 3) the blood-secretory proteins predicted by our model are statistically significant compared with existing blood-based biomarkers of cancer.

血液分泌蛋白的鉴定和血液中蛋白质生物标志物的检测具有重要的临床应用价值。现有的预测血液分泌蛋白的方法主要基于传统的机器学习算法，并且严重依赖于带注释的蛋白特征。与传统的机器学习算法不同，深度学习算法可以从原始数据中自动学习更好的特征表示，并且有望更有望预测血液分泌蛋白。我们提出了一种新型的深度学习模型（DeepHBSP），通过整合二进制分类网络和排名网络，结合单独的氨基酸序列信息来识别血液分泌蛋白，从而将其与转移学习结合在一起。训练步骤中DeepHBSP的损失函数旨在将描述性损失和紧致度损失分别应用于二元分类网络和排名网络。DeepHBSP的特征提取子网由多车道胶囊网络组成。此外，转移学习用于训练少量分泌血液蛋白的高精度通用模型。这项研究的主要贡献如下：1）提出了一种通过集成二进制分类网络和排名网络的新型深度学习架构，该架构优于现有的传统机器学习算法和其他最新的深度学习架构。生物序列分析；2）建议的血液分泌蛋白预测模型仅使用氨基酸序列，克服现有方法对带注释的蛋白质特征的严重依赖；3）与现有的以血液为基础的癌症生物标志物相比，我们的模型预测的血液分泌蛋白具有统计学意义。