Using AI-Driven Target Discovery to Investigate Potential Biological Targets in Circadian
Session Number
1
Advisor(s)
Dr. John Thurmond, IMSA
Location
A155
Discipline
Behavioral and Social Sciences
Start Date
15-4-2026 10:15 AM
End Date
15-4-2026 11:00 AM
Abstract
Delayed Sleep Wake Phase Disorder (DSWPD) is a circadian rhythm sleep disorder (CRSD) characterized by habitual delayed sleep and wake times relative to conventional times, which is often accompanied by impaired daily functioning and increased risk for medical and psychiatric comorbidities. This suggests that the broader CRSDs may influence neurobiological pathways. However, large-scale omics datasets specific to DSWPD are limited. To address this, the present study aims to examine the molecular mechanisms associated with circadian rhythm disruption, which is the mismatch between one’s internal 24-hour circadian clock and the external environment. This study uses the AI-driven target discovery platform PandaOmics to analyze omics datasets associated with circadian rhythm disruption and sleep-wake disorders. Gene expression and pathway analysis are used to identify the potential biological targets associated with circadian dysregulation. The identified biological targets are then evaluated for their potential involvement in psychiatric conditions. These findings will contribute to a deeper understanding of how factors such as light exposure influence circadian regulation and psychiatric outcomes
Using AI-Driven Target Discovery to Investigate Potential Biological Targets in Circadian
A155
Delayed Sleep Wake Phase Disorder (DSWPD) is a circadian rhythm sleep disorder (CRSD) characterized by habitual delayed sleep and wake times relative to conventional times, which is often accompanied by impaired daily functioning and increased risk for medical and psychiatric comorbidities. This suggests that the broader CRSDs may influence neurobiological pathways. However, large-scale omics datasets specific to DSWPD are limited. To address this, the present study aims to examine the molecular mechanisms associated with circadian rhythm disruption, which is the mismatch between one’s internal 24-hour circadian clock and the external environment. This study uses the AI-driven target discovery platform PandaOmics to analyze omics datasets associated with circadian rhythm disruption and sleep-wake disorders. Gene expression and pathway analysis are used to identify the potential biological targets associated with circadian dysregulation. The identified biological targets are then evaluated for their potential involvement in psychiatric conditions. These findings will contribute to a deeper understanding of how factors such as light exposure influence circadian regulation and psychiatric outcomes