Identifying SM-miRNA associations based on layer attention graph convolutional network and matrix decomposition

Predicting Drug-miRNA Associations Combining SDNE with BiGRU

It is well recognized that abnormal miRNA expression can result in drug resistance and pose a challenge to miRNA-based treatments. However, the drug-miRNA associations (DMA) are still incompletely understood. Conventional biological experiments have a high failure rate, lengthy cycle times, and expensive expenditures. Consequently, deep learning-based techniques for predicting DMA have been developed. In this work, we propose a novel method named SDNEDMA for DMA prediction that combines SDNE with BiGRU. The two-channel approach is used to combine the attribute and topological features of miRNAs and drugs. To be more precise, we first model the associations between drugs and miRNAs through the known bipartite network, and then utilize SDNE to obtain the topological features. Meanwhile, BiGRU is employed to acquire miRNA k-mer sequence features and drug ECFP fingerprints. Subsequently, both the topological and attribute features are fused jointly to form final features which is aimed to predict the association score for both them. Multiple features drugs and miRNAs are used at the same time, more information is fused, and the features are more accurate, so the prediction performance is better. The experiments show that SDNEDMA outperforms other state-of-the-art methods, yielding AUC of 0.9641 when we use 5-fold cross-validation on the ncDR dataset. SDNEDMA is additionally employed in a case study, showing how accurate and dependable it is. To sum up, the SDNEDMA has the ability to predict DMA with high accuracy and effectiveness, which is really important for drug development.

HTFSMMA: Higher-Order Topological Guided Small Molecule-MicroRNA Associations Prediction

Small molecules (SMs) play a pivotal role in regulating microRNAs (miRNAs). Existing prediction methods for associations between SM-miRNA have overlooked crucial aspects: the incorporation of local topological features between nodes, which represent either SMs or miRNAs, and the effective fusion of node features with topological features. This study introduces a novel approach, termed high-order topological features for SM-miRNA association prediction (HTFSMMA), which specifically addresses these limitations. Initially, an association graph is formed by integrating SM-miRNA association data, SM similarity, and miRNA similarity. Subsequently, we focus on the local information of links and propose target neighborhood graph convolutional network for extracting local topological features. Then, HTFSMMA employs graph attention networks to amalgamate these local features, thereby establishing a platform for the acquisition of high-order features through random walks. Finally, the extracted features are integrated into the multilayer perceptron to derive the association prediction scores. To demonstrate the performance of HTFSMMA, we conducted comprehensive evaluations including five-fold cross-validation, leave-one-out cross-validation (LOOCV), SM-fixed local LOOCV, and miRNA-fixed local LOOCV. The area under receiver operating characteristic curve values were 0.9958 ± 0.0024 (0.8722 ± 0.0021), 0.9986 (0.9504), 0.9974 (0.9111), and 0.9977 (0.9074), respectively. Our findings demonstrate the superior performance of HTFSMMA over existing approaches. In addition, three case studies and the DeLong test have confirmed the effectiveness of the proposed method. These results collectively underscore the significance of HTFSMMA in facilitating the inference of associations between SMs and miRNAs.

Joint masking and self-supervised strategies for inferring small molecule-miRNA associations

Inferring small molecule-miRNA associations (MMAs) is crucial for revealing the intricacies of biological processes and disease mechanisms. Deep learning, renowned for its exceptional speed and accuracy, is extensively used for predicting MMAs. However, given their heavy reliance on data, inaccuracies during data collection can make these methods susceptible to noise interference. To address this challenge, we introduce the joint masking and self-supervised (JMSS)-MMA model. This model synergizes graph autoencoders with a probability distribution-based masking strategy, effectively countering the impact of noisy data and enabling precise predictions of unknown MMAs. Operating in a self-supervised manner, it deeply encodes the relationship data of small molecules and miRNA through the graph autoencoder, delving into its latent information. Our masking strategy has successfully reduced data noise, enhancing prediction accuracy. To our knowledge, this is the pioneering integration of a masking strategy with graph autoencoders for MMA prediction. Furthermore, the JMSS-MMA model incorporates a node-degree-based decoder, deepening the understanding of the network's structure. Experiments on two mainstream datasets confirm the model's efficiency and precision, and ablation studies further attest to its robustness. We firmly believe that this model will revolutionize drug development, personalized medicine, and biomedical research.