BertNDA: A Model Based on Graph-Bert and Multi-Scale Information Fusion for ncRNA-Disease Association Prediction.

Non-coding RNAs (ncRNAs) are a class of RNA molecules that lack the ability to encode proteins in human cells, but play crucial roles in various biological process. Understanding the interactions between different ncRNAs and their impact on diseases can significantly contribute to diagnosis, prevention, and treatment of diseases. However, predicting tertiary interactions between ncRNAs and diseases based on structural information in multiple scales remains a challenging task.

KGETCDA: an efficient representation learning framework based on knowledge graph encoder from transformer for predicting circRNA-disease associations.

Recent studies have demonstrated the significant role that circRNA plays in the progression of human diseases. Identifying circRNA-disease associations (CDA) in an efficient manner can offer crucial insights into disease diagnosis. While traditional biological experiments can be time-consuming and labor-intensive, computational methods have emerged as a viable alternative in recent years.

Effects of long-term nitrogen addition on dissolved organic matter characteristics in a temperate wetland of Northeast China.

Dissolved organic matter (DOM) plays an indispensable role in ecosystem services and functions in wetlands. While most wetlands have undergone increased nitrogen (N) loading due to intensive human activities, the response of DOM characteristics to long-term N addition remains unexplored. In this study, we assessed the changes in dissolved organic carbon (DOC), NH, NO, dissolved organic N (DON), dissolved total N (DTN), and dissolved total phosphorus (DTP) in surface water and soil pore water at 15 cm depth after 10 years of N addition at four levels (0, 60, 120, and 240 kg N hm year) in a freshwater marsh of Northeast China.