BloodProST: prediction of blood-secretory proteins through self-training.

Accurate identification of proteins secreted into the bloodstream is essential for discovering diagnostic biomarkers and therapeutic targets. A significant challenge is the scarcity of experimentally validated blood-secretory proteins, limiting labeled datasets required for robust model training. To address this issue, we propose BloodProST, a novel machine-learning framework leveraging a self-training strategy to reliably predict blood-secretory proteins. BloodProST iteratively expands the labeled dataset by generating high-confidence pseudo-labels from a large pool of unlabeled protein sequences, thereby progressively enhancing model predictions without continuous manual annotation. At its core, BloodProST incorporates an unsupervised feature selection module based on differential evolution, optimizing the Silhouette score to identify the most discriminative physicochemical and sequence-derived features. Additionally, BloodProST employs a dual-pathway convolutional neural network and long short-term memory (CNN)-(LSTM) architecture: a CNN-based pathway captures local information from pre-constructed features, whereas an LSTM-based pathway extracts high-level sequential dependencies directly from protein sequences. Furthermore, domain-specific biological priors, such as the expected proportion of secretory proteins, are integrated into the model's loss function to guide training toward biologically plausible predictions. Extensive evaluation demonstrates that BloodProST significantly outperforms 14 state-of-the-art models across multiple metrics, achieving superior predictive accuracy, robustness, and interpretability. Validation analyses confirm the biological relevance of predictions through secretion-related markers (e.g. signal peptides and transmembrane regions) and demonstrate effective generalization to other biofluids, such as urine. Collectively, these results illustrate BloodProST's potential as a versatile computational tool for secretion prediction and biomarker discovery across diverse biological fluids.