Syndrome differentiation and treatment is the basic principle of traditional Chinese medicine (TCM) to recognize and treat diseases. Accurate syndrome differentiation can provide a reliable basis for treatment, therefore, establishing a scientific intelligent syndrome differentiation method is of great significance to the modernization of TCM. With the development of biomdical text mining technology, TCM has entered the era of intelligence that based on data, and model training increasingly relies on the large-scale labeled data. However, it is difficult to form a large standard data set in the field of TCM due to the low degree of standardization of TCM data collection and the privacy protection of patients’ medical records. To solve the above problem, a multi-label deep forest model based on an improved multi-label ReliefF feature selection algorithm, ML-PRDF, is proposed to enhance the representativeness of features within the model, express the original information with fewer features, and achieve optimal classification accuracy, while alleviating the problem of high data processing cost of deep forest models and achieving effective TCM discriminative analysis under small samples. The results show that the proposed model finally outperforms other multi-label classification models in terms of multi-label evaluation criteria, and has higher accuracy in the TCM syndrome differentiation problem compared with the traditional multi-label deep forest, and the comparative study shows that the use of PCC-MLRF algorithm for feature selection can better select representative features.

Circular RNAs (circRNAs) play pivotal roles in various biological processes and disease progression, particularly in modulating drug responses and resistance mechanisms. Accurate prediction of circRNA-drug associations (CDAs) is essential for biomarker discovery and the advancement of therapeutic strategies. Although several computational approaches have been proposed for identifying novel circRNA therapeutic targets, their performance is often limited by inadequate modeling of higher-order geometric information within circRNA-drug interaction networks. To overcome these challenges, we propose G2CDA, a geometric graph representation learning framework specifically designed to enhance the identification of CDAs and facilitate therapeutic target discovery. G2CDA introduces torsion-based geometric encoding into the message propagation process of the circRNA-drug network. For each potential association, we construct local simplicial complexes, extract their geometric features, and integrate these features as adaptive weights during message propagation and aggregation. This design promotes a richer understanding of local topological structures, thereby improving the robustness and expressiveness of learned circRNA and drug representations. Extensive benchmark evaluations on public datasets demonstrate that G2CDA outperforms state-of-the-art CDA prediction models, particularly in identifying novel associations. Case studies further confirm its effectiveness by uncovering potential drug interactions with the ALDH3A2 and ANXA2 biomarkers. Collectively, G2CDA provides a robust and interpretable framework for accelerating circRNA-based therapeutic target discovery and streamlining drug development pipelines. Our code are archived in: https://github.com/lizhen5000/G2CDA.

The advancement of deep convolutional neural networks (CNNs) has resulted in remarkable achievements in image super-resolution methods utilizing CNNs. However, these methods have been limited by a narrow perceptual field and often require a high number of parameters and computational complexity, making them unsuitable for resource-constrained devices. Recently, the Transformer architecture has shown significant potential in image super-resolution due to its ability to perceive global features. Yet, the quadratic computational complexity of self-attention mechanisms in these Transformer-based methods leads to substantial computational and parameter overhead, limiting their practical application. To address these challenges, we introduce the Dual Branch Attention Network (DBAN), a novel Transformer model that integrates prior knowledge from traditional dictionary learning with the global feature perception capabilities of Transformers, enabling image super-resolution. Our model features a ”token dictionary” mechanism that uses auxiliary labeling to provide external prior information, enhancing cross-attention and self-attention computations while maintaining a linear relationship between computational complexity and image size. We also propose a Feature Aggregation Module (FAM) that efficiently extracts local contextual information and performs channel feature fusion, substantially enhancing the model’s performance and efficiency. By reasonably arranging the number of modules and the depth of the network, we reduce the complexity of the model. Extensive experiments have demonstrated that our DBAN achieves excellent performance.