Compared with disease biomarkers in blood and urine, biomarkers in saliva have distinct advantages in clinical tests, as they can be conveniently examined through noninvasive sample collection. Therefore, identifying human saliva-secretory proteins and further detecting protein biomarkers in saliva have significant value in clinical medicine. There are only a few methods for predicting saliva-secretory proteins based on conventional machine learning algorithms, and all are highly dependent on annotated protein features. Unlike conventional machine learning algorithms, deep learning algorithms can automatically learn feature representations from input data and thus hold promise for predicting saliva-secretory proteins. We present a novel end-to-end deep learning model based on multilane capsule network (CapsNet) with differently sized convolution kernels to identify saliva-secretory proteins only from sequence information. The proposed model CapsNet-SSP outperforms existing methods based on conventional machine learning algorithms. Furthermore, the model performs better than other state-of-the-art deep learning architectures mostly used to analyze biological sequences. In addition, we further validate the effectiveness of CapsNet-SSP by comparison with human saliva-secretory proteins from existing studies and known salivary protein biomarkers of cancer. The main contributions of this study are as follows: (1) an end-to-end model based on CapsNet is proposed to identify saliva-secretory proteins from the sequence information; (2) the proposed model achieves better performance and outperforms existing models; and (3) the saliva-secretory proteins predicted by our model are statistically significant compared with existing cancer biomarkers in saliva. In addition, a web server of CapsNet-SSP is developed for saliva-secretory protein identification, and it can be accessed at the following URL: http://www.csbg-jlu.info/CapsNet-SSP/. We believe that our model and web server will be useful for biomedical researchers who are interested in finding salivary protein biomarkers, especially when they have identified candidate proteins for analyzing diseased tissues near or distal to salivary glands using transcriptome or proteomics.

中文翻译：

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CapsNet-SSP：用于预测人类唾液分泌蛋白的多道胶囊网络。

与血液和尿液中的疾病生物标志物相比，唾液中的生物标志物在临床测试中具有明显的优势，因为可以通过无创样品采集方便地对其进行检查。因此，鉴定人唾液分泌蛋白并进一步检测唾液中的蛋白质生物标志物在临床医学中具有重要价值。基于常规的机器学习算法只有很少的预测唾液分泌蛋白的方法，并且所有方法都高度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输入数据中自动学习特征表示，因此有望预测唾液分泌蛋白。我们提出了一种基于多车道胶囊网络（CapsNet）的新颖端到端深度学习模型，具有不同大小的卷积内核，仅从序列信息中识别唾液分泌蛋白。拟议的CapsNet-SSP模型优于基于常规机器学习算法的现有方法。此外，该模型的性能要优于其他大多数用于分析生物序列的最新深度学习架构。此外，我们通过与现有研究中的人类唾液分泌蛋白和已知的癌症唾液蛋白生物标记物进行比较，进一步验证了CapsNet-SSP的有效性。这项研究的主要贡献如下：（1）提出了一种基于CapsNet的端到端模型，用于从序列信息中鉴定唾液分泌蛋白。（2）所提出的模型具有更好的性能，并且优于现有模型；（3）与唾液中现有的癌症生物标志物相比，我们的模型预测的唾液分泌蛋白具有统计学意义。此外，还开发了CapsNet-SSP的Web服务器用于唾液分泌蛋白的鉴定，可以通过以下URL访问它：http://www.csbg-jlu.info/CapsNet-SSP/。我们相信，我们的模型和Web服务器将对有兴趣寻找唾液蛋白生物标记物的生物医学研究人员有用，尤其是当他们使用转录组或蛋白质组学确定了候选蛋白来分析唾液腺附近或远端的患病组织时。（3）与唾液中现有的癌症生物标志物相比，我们模型预测的唾液分泌蛋白具有统计学意义。此外，还开发了CapsNet-SSP的Web服务器用于唾液分泌蛋白的鉴定，可以通过以下URL访问它：http://www.csbg-jlu.info/CapsNet-SSP/。我们相信，我们的模型和Web服务器将对有兴趣寻找唾液蛋白生物标记物的生物医学研究人员有用，尤其是当他们使用转录组或蛋白质组学确定了候选蛋白来分析唾液腺附近或远端的患病组织时。（3）与唾液中现有的癌症生物标志物相比，我们模型预测的唾液分泌蛋白具有统计学意义。此外，还开发了CapsNet-SSP的Web服务器用于唾液分泌蛋白的鉴定，可以通过以下URL访问它：http://www.csbg-jlu.info/CapsNet-SSP/。我们相信，我们的模型和Web服务器将对有兴趣寻找唾液蛋白生物标记物的生物医学研究人员有用，尤其是当他们使用转录组或蛋白质组学确定了候选蛋白来分析唾液腺附近或远端的患病组织时。