A new approach for microbe-disease association prediction: incorporating representation learning of latent relationships.

BACKGROUND

Predicting associations between microbes and diseases is crucial for clinical diagnosis and therapy. However, biological experiments are time-intensive, necessitating efficient computational models. Traditional models rely on existing microbe-disease associations, but limited data often restricts their effectiveness. This scarcity of information hinders the construction of a comprehensive association network, prompting the need for innovative solutions.

METHODS

We propose RKGATMDA, a deep learning framework for microbe-disease association prediction. Utilizing a graph attention network, RKGATMDA learns representations from the microbe-disease association network. To address the limitation of insufficient association information, we introduce Random K-Nearest Neighbors to uncover latent relationships, enhancing representation learning. During each training iteration, associations are expanded based on attention scores, and a multi-head attention mechanism integrates diverse features, enabling RKGATMDA to capture comprehensive interactions between microbes and diseases.

RESULTS

Results Experimental results show that RKGATMDA achieves AUC values of 0.8906 in 5-fold cross-validation, 0.8999 in global leave-one-out cross-validation, and 0.7246 in local leave-one-out cross-validation, outperforming previous methods such as ABHMDA, KATZHMDA, LRLSHMDA, BiRWHMDA, and NTSHMDA. Case studies on asthma, colon cancer, and colorectal carcinoma further validate its predictive power.

CONCLUSION

Our findings demonstrate that RKGATMDA effectively predicts microbe-disease associations, with at least 9 out of the top 10 prediction pairs validated by biological evidence. This highlights the potential of RKGATMDA as a valuable tool in microbial-disease research, offering a promising approach for identifying novel associations and advancing our understanding of microbial pathogenesis.