Deep learning based secreted protein prediction

Proteins secreted by various cells and tissues into different body fluids can signal several physiological disorders. However, the degree of complexity in different body fluids and the presence of a large number of proteins in fluids can make studying them with existing proteomics techniques complex and result in large discrepancies between experimental studies. To address this, we developed a deep learning framework called SecBert that identifies secreted proteins in two human body fluids. SecBert uses a sequence-based approach with end-to-end automatic feature extraction for protein classification. Our results show that SecBert performs well, achieving an average area under the ROC curve of 0.94-0.95 on each fluid test dataset.