Probiotic-Disease Association Prediction via Cross-Modal Feature Aggregation

Probiotics are active microorganisms that provide substantial health benefits. They can serve as an alternative or complement to medications, enabling effective and targeted treatment for various diseases. However, traditional experimental methods for screening probiotics are time-consuming and labor-intensive, underscoring the need for efficient computational approaches. While some studies have introduced link prediction methods based on known probiotic-disease associations, these methods often fail to address errors and noise within the dataset and overlook the rich, intrinsic features of probiotics and diseases. To address these limitations, this paper presents MFFPDA, the first deep-learning framework based on multi-feature fusion for predicting probiotic-disease associations. We systematically screened a probiotic-disease association dataset and collected probiotic and disease-related data from various sources. Furthermore, we calculate multiple features of probiotics and diseases, and design some feature extraction and fusion modules to integrate these features. The comparison results demonstrate that MFFPDA surpasses all other comparison methods. Feature visualization results confirmed the necessity and rationale for incorporating multi-source features. Additionally, case studies on colonic pseudo-obstruction and dysentery further validated the effectiveness of MFFPDA, underscoring its reliability as a tool for predicting probiotic-disease associations.