Department of Biomedical Informatics News and Stories

Shawn O’Neil, a data engineer at the Translational and Integrative Sciences Lab (TISLab) at the University of Colorado School of Medicine and training coordinator with the US National Institutes of Health’s National COVID Cohort Collaborative, recently joined the Monarch Initiative, an open-source bioinformatics platform for matching phenotypes to genotypes.

The NCATS National COVID Cohort Collaborative (N3C), which includes DBMI faculty, powered two large studies that reinforced a potential link between metformin and less-severe COVID-19 outcomes. One study of EHRs from more than 6,600 adults found that those with type 2 diabetes who were taking metformin before developing COVID-19 were less likely to be hospitalized, need mechanical ventilation or die. A second study of people with either prediabetes or polycystic ovary syndrome (a condition commonly treated with metformin) showed that metformin use was linked to less severe COVID-19.

“We see physicians working two to four hours every evening on their patient emails after their shift is over, and that’s not sustainable,” says CT Lin, DBMI secondary faculty member and chief medical information officer at University of Colorado Health, which has not yet adopted billing for email visits. “But we worry that patients with complex disease will stop messaging us entirely because of this copay risk.”

Transcriptome-wide association studies have helped uncover the role of individual genes in disease-relevant mechanisms, explain researchers from the CU Department of Biomedical Informatics. However, modern models of the architecture of complex traits predict that gene-gene interactions play a crucial role in disease origin and progression. Researchers introduce PhenoPLIER, a computational approach that maps gene-trait associations and pharmacological perturbation data into a common latent representation for a joint analysis and observe that diseases are significantly associated with gene modules expressed in relevant cell types, and our approach is accurate in predicting known drug-disease pairs and inferring mechanisms of action.