Predicting miRNA-Drug Interactions Based on Multi-source Feature Fusion of Heterogeneous Network

Resistance to treatment remains one of the greatest challenges in cancer therapy. Recent studies have shown that drug sensitivity is closely associated with miRNA expression, highlighting the importance of predicting miRNA-drug interactions (MDIs) in understanding drug resistance mechanisms. Within this study, we propose an innovative method named MSFFMDI, which employs a dual-channel multi-source feature fusion framework based on heterogeneous networks to predict potential MDIs. The first channel focuses on attribute feature extraction. For miRNAs, we integrate the k-mer algorithm with word2vec to transform sequences into low-dimensional embeddings that capture semantic and structural information. For drugs, we utilize the graph isomorphism network to learn molecular structure features, and apply mol2vec to capture chemical and functional sequence features. The second channel extracts topological features by constructing a heterogeneous network based on integrated similarities and known associations between miRNAs and drugs. A graph attention network is used to update node embeddings, and a multi-scale convolutional neural network is employed to further extract topological representations. The features from both channels are fused and reduced via principal component analysis before being used for final prediction. A large number of rich experimental results show that MSFFMDI demonstrates excellent predictive performance on two datasets. Case studies further validate its robust performance. Overall, MSFFMDI provides a powerful and interpretable framework for predicting MDIs and offers potential insights into the mechanisms of drug resistance.