Predicting microbe-drug associations (MDAs) is vital for accelerating drug discovery and optimizing clinical interventions in biomedical research. Traditional laboratory-based methods, though reliable, are constrained by high costs and limited scalability. While many computational approaches have utilized feature similarities to infer MDAs, they often overlook the complex and heterogeneous relationships inherent in biological networks, as well as the challenge posed by imbalanced datasets. In this study, we propose HGBHAN, a novel framework for MDAs prediction using heterogeneous graphs and bidirectional long short-term memory (Bi-LSTM) with hierarchical attention, for robust MDAs prediction. HGBHAN constructs a comprehensive heterogeneous network by integrating microbe and drug similarities with known association information, capturing multi-level structural and sequential dependencies. The model employs Bi-LSTM modules and a hierarchical attention mechanism to learn discriminative node embeddings, while residual connections are incorporated to address the over-smoothing issue in graph neural networks. Extensive experiments conducted on three public benchmark datasets demonstrate that HGBHAN outperforms existing models across multiple evaluation metrics, validating its efficacy in accurately predicting microbe-drug associations.

In the field of biomedicine, predicting microbe-drug associations (MDAs) is crucial for advancing drug discovery and personalized therapy. However, traditional experimental approaches often fall short in meeting requirements for accuracy and scalability. Previous studies have primarily relied on feature similarities to predict microbe-drug associations, largely ignoring the complex interdependencies essential for improved prediction. In this paper, we propose a novel framework named Deep Residual Learning Framework Using Heterogeneous Network Feature (DRL-HNet) for MDAs prediction. DRL-HNet constructs a heterogeneous network representation by integrating relationships and features from multiple data sources for both microbes and drugs. The model incorporates deep residual learning with bottleneck layers to effectively reduce computational complexity while enhancing network expressiveness. Multi-source feature fusion is leveraged to capture complex interaction patterns, while residual connections mitigate overfitting and enhance training efficiency. Extensive cross-validation experiments demonstrate that DRL-HNet outperforms existing models across multiple evaluation metrics, validating its efficacy in accurately predicting microbe-drug associations.

Determining whether deep neural network (DNN) models can reliably recover target functions at overparameterization is a critical yet complex issue in the theory of deep learning. To advance understanding in this area, we introduce a concept we term “local linear recovery” (LLR), a weaker form of target function recovery that renders the problem more amenable to theoretical analysis. In the sense of LLR, we prove that functions expressible by narrower DNNs are guaranteed to be recoverable from fewer samples than model parameters. Specifically, we establish upper limits on the optimistic sample sizes, defined as the smallest sample size necessary to guarantee LLR, for functions in the space of a given DNN. Furthermore, we prove that these upper bounds are achieved in the case of two-layer tanh neural networks. Our research lays a solid groundwork for future investigations into the recovery capabilities of DNNs in overparameterized scenarios.