A Multi-view Divergence-Convergence Feature Augmentation Framework for Drug-related Microbes Prediction
This work addresses a domain-specific problem in drug discovery and precision medicine by improving prediction of drug-microbe associations, representing an incremental advance over existing methods.
The paper tackles the problem of predicting drug-microbe associations by proposing a multi-view Divergence-Convergence Feature Augmentation framework (DCFA_DMP) that integrates association and similarity information through adversarial learning and attention mechanisms, achieving significant performance improvements in prediction tasks.
In the study of drug function and precision medicine, identifying new drug-microbe associations is crucial. However, current methods isolate association and similarity analysis of drug and microbe, lacking effective inter-view optimization and coordinated multi-view feature fusion. In our study, a multi-view Divergence-Convergence Feature Augmentation framework for Drug-related Microbes Prediction (DCFA_DMP) is proposed, to better learn and integrate association information and similarity information. In the divergence phase, DCFA_DMP strengthens the complementarity and diversity between heterogeneous information and similarity information by performing Adversarial Learning method between the association network view and different similarity views, optimizing the feature space. In the convergence phase, a novel Bidirectional Synergistic Attention Mechanism is proposed to deeply synergize the complementary features between different views, achieving a deep fusion of the feature space. Moreover, Transformer graph learning is alternately applied on the drug-microbe heterogeneous graph, enabling each drug or microbe node to focus on the most relevant nodes. Numerous experiments demonstrate DCFA_DMP's significant performance in predicting drug-microbe associations. It also proves effectiveness in predicting associations for new drugs and microbes in cold start experiments, further confirming its stability and reliability in predicting potential drug-microbe associations.