Predicting the Imbalanced Impact of Drugs on Microbial Abundance Using Multi-View Learning and Data Augmentation

The interactions between drugs and microbes affecting microbial abundance can lead to various diseases or reduce the effectiveness of pharmaceutical treatments. Traditional Microbe-Drug Association (MDA) determination through biological assays is time-consuming and costly. With the accumulation of MDA data, computational methods have become a promising approach to infer potential MDAs. Although existing methods focus on predicting whether a drug interacts with a microbe, they can rarely infer whether a drug promotes or inhibits the abundance of a given microbe. Moreover, the extreme imbalance among abundance-promoted, abundance-inhibited, and non-impacted cases remains a challenge for computational prediction methods. To address these issues, we propose a framework for predicting the imbalanced Impact of Drugs on Microbial Abundance by leveraging Multi-view Learning and Data Augmentation, named IDMA-MLDA. IDMA-MLDA employs a novel method of transforming a bipartite graph into a hypergraph, uses hypergraph convolutions to capture high-order vertex neighborhoods (macro-view), and employs graph neural networks to learn individual features of drugs and microbes (micro-view). It integrates features from both macro-view and micro-view to obtain more comprehensive representations, incorporates a data augmentation module to handle class imbalance, and uses a multilayer perceptron to predict the impact of drugs on microbial abundance. We demonstrate the superiority of IDMA-MLDA through comparisons with six baseline methods, and ablation studies affirm the contributions of each key module in IDMA-MLDA's prediction. Furthermore, a comprehensive literature review verifies the abundance types of twelve MDAs predicted by IDMA-MLDA.