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Department of Biomedical Informatics News and Stories


Research    Diversity    Genetics

CU Researchers Weave Deeper Understanding of Diverse Ancestry and Gene Expression

Exploring diverse ancestry is a critical factor in furthering medical research.  

A new study published in Nature Genetics from researchers in the Department of Biomedical Informatics (DBMI) at the University of Colorado School of Medicine, in partnership with the University of California San Francisco and Stanford University, is the largest of its kind that focuses on ancestry correlations with biomedical traits and the first study to examine the role of genetic variants across diverse ancestries in regulating gene expression.

Author Kara Mason | Publish Date May 25, 2023
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Community    Faculty    Diversity    Health equity    Social Justice

Breaking Barriers and Creating Opportunities for Underrepresented People in Data Science

Janani Ravi, PhD, assistant professor in the Department of Biomedical Informatics at the University of Colorado School of Medicine, didn’t intend to challenge the status quo when she began her career in data science. But after several years of working diligently on her research in computational biology as a graduate student at Virginia Tech, as a postdoc at Rutgers Biomedical and Health Sciences, and then as an independent researcher/faculty member at Michigan State University (MSU), she began to see a pattern emerge, particularly on the conference circuit.

Author Toni Lapp | Publish Date November 11, 2022
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Department of Biomedical Informatics In the News


Building off ChatGPT Popularity, Generative AI Starts Finding its Place in Genome Informatics

news outletGenomeWeb
Publish DateSeptember 22, 2023

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.

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National Institutes of Health (NIH)

Can Metformin Treat COVID-19 and Prevent Long COVID? NCATS and Partners Pursue Answers

news outletNational Institutes of Health (NIH)
Publish DateSeptember 18, 2023

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.

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KFF Health News

As More Patients Email Doctors, Health Systems Start Charging Fees

news outletKFF Health News
Publish DateSeptember 14, 2023

“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.”

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Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms

news outletNature
Publish DateSeptember 13, 2023

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.

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