Deep Learning for LncRNA-Disease Association Prediction: Challenges and Future Directions

With the rapid development of bioinformatics and high-throughput sequencing technology, a large amount of biological data and related databases have been made public, providing powerful data support for researchers to explore the association between long non-coding RNA (LncRNA) and diseases through computational methods. Currently, there is increasing evidence showing that LncRNA plays important roles in almost the entire life cycle of cells and in many key biological processes through different mechanisms. Due to the various functional roles of LncRNA in genome regulation, predicting the association between LncRNA and diseases (LDA) has attracted more and more interest to explain potential disease mechanisms. Therefore, clarifying the relationship between LncRNA and diseases not only helps biologists understand disease mechanisms, but also provides new ideas and solutions for disease diagnosis, treatment, prognosis and prevention.

Although the importance of LDA prediction has become increasingly prominent with the deepening of LncRNA-Disease association research, the existing experimental methods are not only expensive but also time-consuming, which limits the application and development of LDA prediction. Therefore, LDA prediction based on deep learning models has received more and more attention and research. In this report, I combined the application of existing deep learning models in LDA prediction, analyzed their potential shortcomings, and explored new prediction methods and technical routes, providing some ideas for new models. In the future, we can further improve deep learning models to improve their prediction accuracy and efficiency, and combine with other methods to analyze from multiple perspectives, hoping to further promote the progress of LncRNA-Disease association research.