Aim: Our research aims to establish an assessment model for insomnia risk factors and identify biomarkers associated with insomnia and its complications.
Scientific rationale: Through deep learning, a risk prediction model can be established to identify high-risk factors for insomnia. Bioinformatics can process and analyze large-scale data. For example, methods such as gene expression profiling, pathway analysis, transcription factor recognition, and protein-protein interaction network analysis can identify potential biomarkers related to insomnia.
project duration: A year
public health impact: The identification of high-risk factors for insomnia will enable early intervention and promote public health education. Furthermore, the biomarkers of insomnia and its complications, as well as the features observed in neuroimaging, will enhance our understanding of the mechanisms underlying insomnia and guide clinical practice.
