AGCLNDA: Enhancing the Prediction of ncRNA-Drug Resistance Association Using Adaptive Graph Contrastive Learning.

Abstract

Non-coding RNAs (ncRNAs), which do not encode proteins, have been implicated in chemotherapy resistance in cancer treatment. Given the high costs and time requirements of traditional biological experiments, there is an increasing need for computational models to predict ncRNA-drug resistance associations. In this study, we introduce AGCLNDA, an adaptive contrastive learning method designed to uncover these associations. AGCLNDA begins by constructing a bipartite graph from existing ncRNA-drug resistance data. It then utilizes a light graph convolutional network (LightGCN) to learn vector representations for both ncRNAs and drugs. The method assesses resistance association scores through the inner product of these vectors. To tackle data sparsity and noise, AGCLNDA incorporates learnable augmented view generators and denoised view generators, which provide contrastive views for enhanced data augmentation. Comparative experiments demonstrate that AGCLNDA outperforms five other advanced methods. Case studies further validate AGCLNDA as an effective tool for predicting ncRNA-drug resistance associations.