Department of Biomedical Informatics In the News

The 2024 recipients: Muyinatu A. Lediju Bell, a biomedical engineer at Johns Hopkins University; Katrina G. Claw, a genetic scientist at the University of Colorado School of Medicine's Department of Biomedical Informatics; and Rebecca Kramer-Bottiglio, an engineer working in robotics at Yale University, were recognized for their innovative approaches and leadership in their respective fields and for pushing the boundaries of science in truly novel ways.

Through an R21 grant from the National Institutes of Health, Nikita Pozdeyev, MD, assistant professor of biomedical informatics, Chris Gignoux, PhD, professor of biomedical informatics, and Bryan Haugen, MD, professor of medicine and head of the Division of Endocrinology, Metabolism, and Diabetes, will study new strategies that could pave the way for personalized management of thyroid nodules, inform future mechanistic studies of thyroid cancer, and lead to a clinical trial of an ultrasound and genetic thyroid nodule classifier.

In this World Shared Practice Forum Podcast, authors of the newly released publication, International Consensus Criteria for Pediatric Sepsis and Septic Shock, review their research and findings for treating and caring for children with sepsis and septic shock. The group, including DBMI professor Tell Bennett, MD, discuss how using the novel Phoenix Sepsis Score guided the development of this new globally applicable research model.

In the world of pet care, DNA tests are increasingly used to trace our pets’ ancestry. However, a University of Colorado study led by DBMI founding chair Casey Greene, PhD, and Halie Rando, PhD, has revealed significant inaccuracies in canine DNA testing. This raises concerns among dog owners about the reliability of these tests.

Katrina Claw, PhD, assistant professor of biomedical informatics, is one of 11 researchers in a career feature in Nature on March 12 explaining what motivates their academic lives. “My dad was a heavy-equipment operator for more than 40 years, and in our tribe’s traditional ceremonies, he always prayed for me to have a job in which I would stay clean all day and have an office,” Katrina says. “I not only have an office with views of the Rocky Mountains, I also have a laboratory full of pristine equipment and lead a successful research programme that trains students and collaborates with Native American communities on pharmacogenomics and the cultural, ethical, legal and social implications of genomics research.”

A recent study conducted by researchers at the Department of Biomedical Informatics DBMI at the University of Colorado School of Medicine delved into the intricacies of DTC dog DNA testing, shedding light on the complexities and challenges inherent in deciphering the genetic code of man’s best friend.

Researchers across the nation, including DBMI faculty member Toan Ong, PhD, find that the most recent COVID-19 booster shot reduces adults’ risk of moderate or severe COVID by more than half. The conclusion comes from a nationwide data study from September 2023 through January 2024, a period of JN.1 variant dominance.

DBMI chair Casey Greene, interim director of the Center for Personalized Medicine, said the center’s work, in addition to benefiting researchers and patients, has the potential to reduce health care spending by reducing hospitalizations and other medical care related to bad drug-gene interactions. “There’s not that many win-wins in the health care system where payers can pay less and patients can get better care,” Greene said. “This is actually a win-win.”

Tell Bennet, MD, MS, professor of biomedical informatics, explains the data analysis required to help enable clinicians to diagnose sepsis in children. The members of the Society of Critical Care Medicine (SCCM) Pediatric Sepsis Definitions Task Force wanted not only to update the criteria specifically for children but also to establish parameters that clinicians could use whether they practice in countries or areas with more or fewer resources, because sepsis is a major public health burden throughout the world.

Paul Norman, PhD, professor of biomedical informatics, explains how studying the bony labyrinth has helped yield new insights into who survived past plague outbreaks and why.

“We leveraged a heavily data-driven approach in order to develop and validate these new criteria,” Tellen D.Bennett, MD, MS, a professor of biomedical informatics and pediatric critical care at the CU School of Medicine, said in a release.

An international research team led by Tell Bennett, MD, MS, professor of biomedical informatics and pediatric critical care at the University of Colorado School of Medicine, released new diagnostic criteria for sepsis in children this week, marking the first update to the pediatric sepsis definition in nearly two decades.

“We’re one of the only institutions in the world that has accomplished the dual-purpose of using genetic information to accelerate scientific discovery in research while providing actionable clinical results to patients that can lead to preventative measures for diseases or inform on potential medication reactions,” said the paper’s senior author Chris Gignoux, PhD, MS, professor of Biomedical Informatics at CU Anschutz and the Director of Research for CCPM.  

Given the potential to develop scientific and clinical knowledge and the NIH emphasis on data sharing and reuse, there is a need for inexpensive and computationally lightweight methods for data sharing and hosting data challenges. To fill that gap, DBMI researchers developed a workflow that allows for reproducible model training, testing, and evaluation.

Iain R. Konigsberg, PhD, a research instructor in the CU Department of Biomedical Informatics, tells SELF that if you have a high polygenic risk score for a disease, then it’s extra-important for you to lean into lifestyle choices that support your well-being and address the risk factors associated with that condition.

Katrina Claw, PhD, a Navajo geneticist and DBMI assistant professor, explains that Western science and medicine have historically ignored and underestimated the traditional knowledge of Native people.

DBMI secondary faculty member CT Lin, MD, says he and others at UCHealth have developed a system they say is saving hundreds of lives a year by catching sepsis cases early. And they’ve built this system using the technology that is poised to revolutionize how doctors deliver — and how patients receive — health care: artificial intelligence.

New research led by DBMI secondary faculty member Fan Zhang, PhD, and Anna Helena Jonsson, MD, PhD, may lead to new targeted treatments for rheumatoid arthritis (RA), an autoimmune disease that causes joint inflammation and destruction.

“The methods proposed have the power to advance healthcare best practices in personalized medicine on national, regional and local scales,” says DBMI professor Melissa Haendel, PhD, chief research informatics officer with CU Anschutz and the principal investigator of the grant.

University of Colorado Anschutz Medical Campus and partners will connect new types of information to All of Us participant data to help researchers better understand the drivers of health and disease. DBMI associate research professor Anita Walden will lead management of the program. “I am enthusiastic about this leading-edge project and joining the All of Us team,” she said in a statement.

“Some genes we identified are related to sensory pathways – including those for taste, smell, and texture – and may also increase the reward response in the brain,” said lead author Joanne Cole, PhD, assistant professor of Biomedical Informatics department at the University of Colorado School of Medicine. 

The National Institutes of Health’s All of Us Research Program has awarded $30 million to DBMI faculty at the University of Colorado Anschutz Medical Campus to establish the Center for Linkage and Acquisition of Data (CLAD). The All of Us Research Program is a historic effort to enroll at least 1 million people who reflect the diversity of the United States.

Joanne Cole, PhD, an assistant professor of biomedical informatics at the University of Colorado School of Medicine who was not involved in the research, praised the paper for clearly articulating its definitions of vegetarianism and rigorously analyzing the genome.

Diabetes researchers, including from the CU Department of Biomedical Informatics, report evidence, albeit low certainty, that those with poorer health status, particularly those with prediabetes at baseline, tend to benefit more from type 2 diabetes prevention strategies compared to healthier counterparts.

Type 1 diabetes researchers, including DBMI faculty, identify and summarize 75 manuscripts, 15 describing 11 prevention trials for individuals with increased risk for type 1 diabetes, and 60 describing treatments aimed at preventing beta cell loss at disease onset.

DBMI assistant professor Joanne Cole, PhD, explains her research that shows people’s genes and the types of food they like, and aversions to certain foods, have long been understood to have genetic ties.

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.

After tracking calorie-control dieters and intermittent fasters for three months, both had improved microbiome diversity, said study author Maggie Stanislawski, an assistant professor in the CU Department of Biomedical Informatics.

DBMI geneticist Joanne Cole, PhD, explains the role of genetics and other factors in food preference. 

Researchers in the CU Department of Biomedical Informatics evaluate associations among gut microbiota (MB), DNA methylation (DNAme), and diet prior to and during a behavioral weight loss intervention.

Research by data scientists in the Department of Biomedical Informatics and others in the CU School of Medicine aims to understand the relationship between changes in diet and the microbiome. Some of this work uncovers possible routes by which alterations in gut microbiota may influence metabolism during a dietary weight loss intervention.

New research from data scientists in the CU Department of Biomedical Informatics shows that intermittent fasting and calorie restriction change the microbiome composition in the gut, which could affect other functions in the body.

DBMI assistant professor Gregory Way, PhD, explains how software gardeners create a multidimensional understanding of software development, while strengthening development practices.

You are what you eat—and what you eat may be encoded in your DNA. Studies have indicated that your genetics play a role in determining the foods you find delicious or disgusting. But exactly how big a role they play has been difficult to pin down. “Everything has a genetic component, even if it’s small,” says Joanne Cole, a geneticist and an assistant professor in the Department of Biomedical Informatics at the University of Colorado School of Medicine. 

"Dietary intake is influenced by an array of environmental factors, including socio-economic status, upbringing, culture, learned behavior, etc.," DBMI geneticist Joanne Cole, a professor in the CU School of Medicine who led the research, told Newsweek. "However, there is a real genetic influence on the foods we choose to eat, albeit small."

In one of the first large-scale studies of genes related to diet, researchers at the CU Department of Biomedical Informatics have uncovered almost 500 genes that appear to directly influence the foods we eat. The findings represent an important step toward using a person's genetics to develop precision nutrition strategies that help improve health or prevent disease.

“Some genes we identified are related to sensory pathways — including those for taste, smell and texture — and may also increase the reward response in the brain,” said research team leader Joanne Cole, an assistant professor in the University of Colorado Department of Biomedical Informatics.

Research team leader Joanne Cole, PhD, an assistant professor in the CU School of Medicine Department of Biomedical Informatics, told South West News Service, “Some genes we identified are related to sensory pathways — including those for taste, smell, and texture — and may also increase the reward response in the brain."

“Leadership at CU and at UCHealth are aligned in terms of understanding that AI is going to transform health care,” said  Casey Greene, who chairs the Department of Biomedical Informatics and directs the Center for AI Health. “Being at the forefront of it is going to be important, and the question is how do we operationalize that general concept into practice?”

CT Lin, MD, chief medical informatics officer of UCHealth and secondary DBMI faculty member, explains the "perfect match" between AI and humans at UCHealth. 

Researchers in the CU Department of Biomedical Informatics have identified a potential new immune checkpoint receptor that could lead to treatments for diseases such as lung and bowel cancer and autoimmune conditions including IBD.

Researchers may use artificial intelligence (AI) language models such as ChatGPT to write and revise scientific manuscripts, according to a new announcement from the International Committee of Medical Journal Editors (ICMJE). These tools should not be listed as authors, and researchers must denote how AI-assisted technologies were used, the committee said.

Researchers in the CU Department of Biomedical Informatics help students, researchers, funders, and publishers better understand the state of our collective scientific ignorance (known unknowns) in order to help accelerate research through the continued illumination of and focus on the known unknowns and their respective goals for scientific knowledge. 

CU Department of Biomedical Informatics professor and founding chair Casey Greene, PhD, along with other researchers outline the challenges and emerging solutions for using machine learning (ML) for small sample sets, specifically in rare diseases. Advances in ML methods for rare diseases are likely to be informative for applications beyond rare diseases for which few samples exist with high-dimensional data.

CU Department of Biomedical Informatics researchers helped explore ancestry-related differences in the genetic architecture of whole-blood gene expression using whole-genome and RNA sequencing data from 2,733 African Americans, Puerto Ricans and Mexican Americans.

The Phenopacket Schema developed by DBMI researchers is flexible and can represent clinical data for any kind of human disease including rare disease, complex disease, and cancer. It also allows consortia or databases to apply additional constraints to ensure uniform data collection for specific goals.

CT Lin, MD, a secondary professor in the Department of Biomedical Informatics, is among influencers and health IT leaders paving the way toward health equity and transforming healthcare’s approach to data.

As part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, researchers, including University of Colorado Department of Biomedical Informatics professor Melissa Haendel, with the National COVID Cohort Collaborative (N3C) devised and trained a machine learning-based phenotype to identify patients highly probable to have Long COVID.

CT Lin, a secondary faculty member for the Department of Biomedical Informatics at the CU School of Medicine, looks at the 21st Century Cures Act Final Rule that mandates the immediate electronic availability of test results to patients, as concerns remain about unintended effects of releasing abnormal test results to patients.

University of Colorado Anschutz biomedical informatics researcher Casey Greene wrote that just as Google led people to develop skills in finding information on the internet, AI language models will lead people to develop skills to get the best output from the tools.

Maggie Stanislawski, PhD, assistant professor of biomedical informatics at the CU School of Medicine, discussing the benefits of breastfeeding, including a lower risk of obesity for baby, with the British Nutrition Foundation.

Nick Dwork, PhD, and a team of researchers present a method to determine the density compensation values of the non-uniform DFT of type I. To the best knowledge of the authors, this is the first method for determining the density compensation values that best approximate a Dirac delta function in space.

Casey Greene, PhD, professor and chair of the CU Department of Biomedical Informatics, was published in Nature Communications Biology on the journal's fifth anniversary. He and the other two researchers find that quantile and Training Distribution Matching normalization allow for supervised and unsupervised model training on microarray and RNA-seq data simultaneously.

FASEB and the Office of Data Science Strategy at the National Institutes of Health (NIH) announced the inaugural recipients of the FASEB DataWorks! Prize. The prize showcases teams’ exemplary achievements in biological and biomedical research that were made possible through data management and sharing. Three teams from the University of Colorado DBMI were named winners.

Winners of the the Federation of American Societies for Experimental Biology (FASEB) DataWorks! Challenge included three teams from the CU Department of Biomedical Informatics. Congratulations to researchers in the National Covid Cohort Collaborative (N3C), the Monarch Initiative, and the Krishnan Lab.

In June 2021, researchers at the University of Colorado School of Medicine in Aurora turned a magnifying glass on Nature’s written journalism. Computational biologists Natalie Davidson and Casey Greene reported that most of the individuals we quoted were male. The journal’s editorial teams collectively pledged to work harder to improve the diversity of those whose words or voices are quoted or paraphrased in our reporting. The first results are now in.

In December, computational biologists Casey Greene and Milton Pividori embarked on an unusual experiment: they asked an assistant who was not a scientist to help them improve three of their research papers. This assistant, as Greene and Pividori reported in a preprint on 23 January, is not a person but an artificial-intelligence (AI) algorithm called GPT-3, first released in 2020.

Says Casey Greene (@GreeneScientist), who has mainly been active on Twitter, he too spots research on Twitter he would otherwise miss. At the University of Colorado School of Medicine, he is chair of the Department of Biomedical Informatics and the founding director of the Center for Health AI. He likes how Twitter lowers the barrier to entry for important conversations. 

The Conversation asked five artificial intelligence researchers to discuss how large language models are likely to affect artists and knowledge workers. And, as our experts noted, the technology is far from perfect, which raises a host of issues – from misinformation to plagiarism – that affect human workers.

Each day, around 350 people in the United States die from lung cancer. Many of those deaths could be prevented by screening with low-dose computed tomography (CT) scans. But scanning millions of people would produce millions of images, and there aren’t enough radiologists to do the work. Even if there were, specialists regularly disagree about whether images show cancer or not. 

The International Classification of Diseases 10th Revision (ICD-10) and its predecessors have been essential code sets in medical records for decades, but geneticists, medical informaticians, and coders alike are finding the current system to be wholly inadequate for the age of precision medicine.

Nicholas Dwork, PhD, assistant professor in the Department of Biomedical Informatics at the University of Colorado School of Medicine, has filed a provisional patent for a technology that could increase scan speeds of three-dimensional magnetic resonance imaging (MRI). The invention could lead to faster results, increase the clinical applications of MRIs, and ultimately improve patient care.

The discrete curvelet transform decomposes an image into a set of fundamental components that are distinguished by direction and size and a low-frequency representation. The curvelet representation of a natural image is approximately sparse; thus, it is useful for compressed sensing. 

Obesity, defined as excessive fat accumulation that represents a health risk, is increasing in adults and children, reaching global epidemic proportions. Body mass index (BMI) correlates with body fat and future health risk, yet differs in prediction by fat distribution, across populations and by age. Nonetheless, few genetic studies of BMI have been conducted in ancestrally diverse populations. Gene expression association with BMI was assessed in the Multi-Ethnic Study of Atherosclerosis (MESA) in four self-identified race and ethnicity (SIRE) groups to identify genes associated with obesity.

Chronic stress is a risk factor for preterm birth, however objective measures of stress in pregnancy are limited. Maternal stress biomarkers may fill this gap. Steroid hormones and neurosteroids such as allopregnanolone (ALLO) play important roles in stress physiology and pregnancy maintenance and therefore may be promising for preterm birth prediction.

Understanding household and family memberships has always been important in distinguishing the influence of shared genetics and environments on clinical risks and patient outcomes. Electronic health records (EHR) often include vast information about individuals that, if linked with that of their household or family members, could provide rich and detailed environmental and family history information to support scientific discovery, and patient-centered outcomes research.

Echocardiography is the primary tool in pediatric cardiology for diagnosing and managing congenital heart disease (CHD). Accurate and reliable echocardiographic assessment is critical in clinical decision making. Artificial intelligence (AI), in particular deep learning, has the potential to reduce labor and provide automated assessment of CHD. 

There are currently >1.3 million human –omics samples that are publicly available. This valuable resource remains acutely underused because discovering particular samples from this ever-growing data collection remains a significant challenge. The major impediment is that sample attributes are routinely described using varied terminologies written in unstructured natural language.

American Indian and Alaska Native (AI/AN) populations have long experienced health disparities such as higher rates of diabetes, certain cancers, and mental health conditions than those of other Americans. One contributing factor in these disparities is underrepresentation of AI/AN populations in biomedical science—as study participants, researchers, and health professionals.

Morphological and gene expression profiling can cost-effectively capture thousands of features in thousands of samples across perturbations by disease, mutation, or drug treatments, but it is unclear to what extent the two modalities capture overlapping versus complementary information.

A new paper by researchers at the University of Colorado Anschutz Medical Campus expounds on the importance of using multiple data types in drug discovery. For the paper, published in Cell Systems, the researchers screened over 1,000 drugs tested in six doses. The results, according to the team, demonstrate that the two most popular methods—gene expression and cell morphology—should be used in tandem.

Pandemics and disease outbreaks put a spotlight on the hurdles researchers face to get a drug on the shelves. From finding prospective drug candidates to balancing time and financial pressures with ensuring safety and efficacy, there are many aspects of drug development that determine whether a treatment ever makes it out of the lab.

Joanne Cole, PhD, associate professor of biomedical informatics at the University of Colorado School of Medicine, has been published in Nature Genetics as an author of this study regarding diabetes diagnosis based on hemoglobin A1C levels.

Polygenic scores (PGSs) combine the effects of common genetic variants to predict risk or treatment strategies for complex diseases. Adding rare variation to PGSs has largely unknown benefits and is methodically challenging. 

Acute respiratory distress syndrome (ARDS) occurs when fluid builds up in the air sacs of the lungs, preventing them from opening completely and depriving the body of necessary oxygen. The condition causes significant problems in clinical management and develops in roughly eight to 10 percent of patients in the intensive care unit. Most people with ARDS require intubation and mechanical ventilation in order to survive. “The problem is, when you put people on a mechanical ventilator, it inflates your lungs differently than your diaphragm does,” Jake Stroh of the University of Colorado Anschutz Medical Campus said. “So it increases the risk of ventilator-induced lung injury (VILI).” 

ACM’s Special Interest Group on High Performance Computing (SIGHPC) has announced the eleven recipients of the ACM SIGHPC Computational and Data Science Fellowships for 2022. The fellowships are highly competitive, and are awarded after a rigorous merit review. The fellowship program, previously funded by Intel, is now funded exclusively by SIGHPC.

Many people—potentially millions globally—are still living with the long-term effects of a COVID-19 infection, known as the post-acute sequelae of SARS-CoV-2 infection (PASC), including Long COVID. This is a public health issue that is in desperate need of answers. Research is essential to address the many puzzling aspects of Long COVID and guide us to effective responses that protect the nation’s long-term health.

Casey Greene, PhD, chair of the University of Colorado School of Medicine’s Department of Biomedical Informatics, is working toward a future of “serendipity” in healthcare – using artificial intelligence (AI) to help doctors receive the right information at the right time to make the best decision for a patient.

The University of Colorado (CU) School of Medicine has announced the launch of its new Department of Biomedical Informatics (DBMI), which will focus on addressing health disparities and improving healthcare quality using big data and artificial intelligence (AI).

Common genetic variants have been associated with idiopathic pulmonary fibrosis (IPF). We have established a relationship of the common IPF genetic risk variants rs35705950 and rs2076295 with respective changes in MUC5B and DSP expression and methylation. These results provide additional evidence that both MUC5B and DSP are involved in the etiology of IPF.

An ultrasound-based artificial intelligence classifier of thyroid nodules identified benign nodules with sensitivity similar to fine-needle aspiration, according to data presented at ENDO 2022.

A machine-learning algorithm that analyzes thyroid-nodule ultrasound images correctly identifies likely cancerous nodules 97% of the time - which is on par with thyroid biopsy with fine-needle aspiration, according to research presented at the Endocrine Society annual meeting.

Most of us have experienced some form of medical imaging, whether it was at an eye appointment or after a broken bone. These images might contain more information than meets the eye. Things artificial intelligence can detect.

Children with COVID-19 often get less sick than adults, but some are at risk for serious illness or even death. In a recent study published in the journal JAMA Network Open, NCATS-supported researchers found traits that put children more at risk for severe COVID-19 complications. These traits can help doctors decide whether children diagnosed with COVID-19 should receive different treatment to avoid hospitalization.

A study by researchers at the University of Colorado Anschutz Medical Campus used electronic health records to answer a still difficult question: just what is long COVID? The work also will help recruit patients for a major study of the condition.