Publications by authors named "Zhouhang Yuan"
Article Synopsis
- Understanding the relationships between microRNAs, genes, and diseases is essential for improving disease diagnosis and developing targeted therapies, and computational strategies can effectively predict these associations at a lower cost than traditional methods.
- Many existing techniques focus on specific association types, like miRNA-disease or gene-disease connections, but there is a need for a comprehensive approach that utilizes multiple data sources for more accurate predictions.
- The proposed GlaHGCL framework enhances prediction accuracy by using global and local contrastive learning techniques to improve node embeddings in complex biological graphs, showing superior results in experiments and revealing new associations.
Microbial keratitis, a nonviral corneal infection caused by bacteria, fungi, and protozoa, is an urgent condition in ophthalmology requiring prompt treatment in order to prevent severe complications of corneal perforation and vision loss. It is difficult to distinguish between bacterial and fungal keratitis from image unimodal alone, as the characteristics of the sample images themselves are very close. Therefore, this study aims to develop a new deep learning model called knowledge-enhanced transform-based multimodal classifier that exploited the potential of slit-lamp images along with treatment texts to identify bacterial keratitis (BK) and fungal keratitis (FK).
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