Hyperspectral image (HSI) denoising is an essential preprocessing step for downstream applications. Although Vision Transformer based approaches show impressive denoising performance through self-similarity modeling, these methods still fail to exploit spatial and spectral correlations while ensuring flexibility and efficacy. To address this issue, we propose a Hyperspectral Denoising Transformer using Low-Rank Prompt (HyLoRa), simultaneously taking the spatial self-similarity and spectral low-rank property into account for HSI denoising. Specifically, to fully utilize intrinsic similarity in spatial domain, we perform cross-shaped window based spatial self-attention for effectively modeling local and global similarity. Moreover, to exploit Low-Rank inductive bias, we integrate a Low-Rank Prompt module into attention calculation for counting corrected low-dimensional vector from a large collection of HSIs. This helps to better refine underlying noise-free structure representations. Compared to existing works, powerful capabilities for modeling spatial and spectral correlations can be built to correct low-rank representation in the feature space. Extensive experiments on both simulated and real remote sensing noise demonstrate that our HyLoRa consistently surpasses the state-of-the-art methods.

MicroRNAs (miRNAs) have significant implications in diverse human diseases and have proven to be effectively targeted by small molecules (SMs) for therapeutic interventions. However, current SM–miRNA association prediction models do not adequately capture SM/miRNA similarity. Matrix completion is an effective method for association prediction, but existing models use nuclear norm instead of rank function, which has some drawbacks. Therefore, we proposed a new approach for predicting SM–miRNA associations by utilizing the truncated schatten p-norm (TSPN). First, the SM/miRNA similarity was preprocessed by incorporating the Gaussian interaction profile kernel similarity method. This identified more SM/miRNA similarities and significantly improved the SM–miRNA prediction accuracy. Next, we constructed a heterogeneous SM–miRNA network by combining biological information from three matrices and represented the network with its adjacency matrix. Finally, we constructed the prediction model by minimizing the truncated schatten p-norm of this adjacency matrix and we developed an efficient iterative algorithmic framework to solve the model. In this framework, we also used a weighted singular value shrinkage algorithm to avoid the problem of excessive singular value shrinkage. The truncated schatten p-norm approximates the rank function more closely than the nuclear norm, so the predictions are more accurate. We performed four different cross-validation experiments on two separate datasets, and TSPN outperformed various most advanced methods. In addition, public literature confirms a large number of predictive associations of TSPN in four case studies. Therefore, TSPN is a reliable model for SM–miRNA association prediction.

In the rapidly evolving field of consumer healthcare, the exploration of non-coding RNA interactions is crucial for drug development and personalized therapy. However, through traditional experimental validation methods, it is usually costly in terms of labor and money. In this article, we propose a efficient smart model utilizing multi-perspective weighted matrix factorization with global and local interactive-based random walk (MWMF-GLRW) to assist personalized treatment. First, only known interaction information was used to compute noncoding RNA similarities and perform fusions to ensure simplicity and generalizability of the model. Second, we innovatively develop a multi-perspective weighted matrix factorization technique. This method extracts key features while preserving the matrix structure, effectively enhancing the robustness and formative nature of miRNA-lncRNA edges between miRNA and lncRNA networks. Third, we introduce a new random walk method that considers both global information and local details of heterogeneous networks. This iterative interaction mechanism dynamically adjusts the model, enhancing its robustness and accuracy. Experiments show that MWMF-GLRW surpasses the state-of-the-art model on three datasets using only known interaction information. Notably, the simplicity of our methodology, combined with its high predictive efficiency, makes it well-suited for application in medical electronic devices aimed at promoting healthy patient consumption.