MIFNDRA: an innovative knowledge-enhanced multimodal fusion and graph learning framework for predicting drug resistance-related ncRNAs

Drug resistance is a significant challenge in cancer treatment, greatly impacting treatment efficacy. Non-coding RNAs (ncRNAs) play crucial roles in mediating drug resistance, yet few computational models effectively predict drug resistance-associated ncRNAs. Existing methods often overlook the complex sequence patterns of ncRNA and their intricate interrelationships, resulting in suboptimal performance. To address these challenges, we propose MIFNDRA, a multimodal integrative framework that jointly models ncRNA and drug features to identify drug resistance-related ncRNAs. MIFNDRA employs a pre-trained Graph Isomorphism Network to extract drug structural features and a pre-trained SpliceBERT model to encode ncRNA sequences. It also incorporates various similarity features for both drugs and ncRNAs, while improving representation through a novel ncRNA interaction network that includes interactions between different ncRNA types as a strategy for knowledge enhancement. By leveraging advanced graph learning techniques, including residual GraphSAGE and contrastive learning, the model improves the identification of drug resistance-associated ncRNAs. Additionally, we curated a new benchmark dataset pairing ncRNA sequences with drug SMILES and resistance annotations. Comprehensive experiments demonstrate that MIFNDRA achieved state-of-the-art performance. Case studies on cisplatin and gemcitabine further validate the model's robustness and potential in advancing drug resistance research and drug development. The data and code required for this work are available at https://github.com/SJNNNN/MIFNDRA.