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.

Plant vacuoles are essential organelles in the growth and development of plants, and accurate identification of their proteins is crucial for understanding their biological properties. In this study, we developed a novel model called GraphIdn for the identification of plant vacuole proteins. The model uses SeqVec, a deep representation learning model, to initialize the amino acid sequence. We utilized the AlphaFold2 algorithm to obtain the structural information of corresponding plant vacuole proteins, and then fed the calculated contact maps into a graph convolutional neural network. GraphIdn achieved accuracy values of 88.51% and 89.93% in independent testing and fivefold cross-validation, respectively, outperforming previous state-of-the-art predictors. As far as we know, this is the first model to use predicted protein topology structure graphs to identify plant vacuole proteins. Furthermore, we assessed the effectiveness and generalization capability of our GraphIdn model by applying it to identify and locate peroxisomal proteins, which yielded promising outcomes. The source code and datasets can be accessed at https://github.com/SJNNNN/GraphIdn .