Publications by authors named "Daying Lu"
DGNMDA: Dual Heterogeneous Graph Neural Network Encoder for miRNA-Disease Association Prediction.
Bioengineering (Basel)
November 2024
In recent years, numerous studies have highlighted the pivotal importance of miRNAs in personalized healthcare, showcasing broad application prospects. miRNAs hold significant potential in disease diagnosis, prognosis assessment, and therapeutic target discovery, making them an integral part of precision medicine. They are expected to enable precise disease subtyping and risk prediction, thereby advancing the development of precision medicine.
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- Scientific evidence underscores the importance of miRNA-disease association research for understanding diseases and creating new diagnostics, with bioinformatics focusing on identifying these associations.
- Advancements in graph neural networks (GNNs) have improved methodologies in this area, although existing methods face challenges with data noise and integrating information.
- The proposed HGTMDA framework uses random walk with restart-based association masking and an enhanced GCN-Transformer model to effectively identify miRNA-disease associations, demonstrating superior performance in real-world applications, particularly in cancer studies.