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.

Prediction of MicroRNA-Disease Potential Association Based on Sparse Learning and Multilayer Random Walks.

More and more studies have shown that microRNAs (miRNAs) play an indispensable role in the study of complex diseases in humans. Traditional biological experiments to detect miRNA-disease associations are expensive and time-consuming. Therefore, it is necessary to propose efficient and meaningful computational models to predict miRNA-disease associations.

Prediction of potential drug-microbe associations based on matrix factorization and a three-layer heterogeneous network.

Microbes in the human body are closely linked to many complex human diseases and are emerging as new drug targets. These microbes play a crucial role in drug development and disease treatment. Traditional methods of biological experiments are not only time-consuming but also costly.

Integrally Cooperative Spatio-Temporal Feature Representation of Motion Joints for Action Recognition.

In contemporary research on human action recognition, most methods separately consider the movement features of each joint. However, they ignore that human action is a result of integrally cooperative movement of each joint. Regarding the problem, this paper proposes an action feature representation, called Motion Collaborative Spatio-Temporal Vector (MCSTV) and Motion Spatio-Temporal Map (MSTM).