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Machine learning tool identifies rare, undiagnosed immune disorders through patients’ electronic health records
By Kevin McClanahan
Researchers say a machine learning tool can identify many patients with rare, undiagnosed diseases years earlier, potentially improving outcomes and reducing cost and morbidity. The findings, led by researchers at UCLA Health, are described in Science Translational Medicine.
MSCR Alumnus Recognized for Advancing Diversity in Healthcare
Dr. Bruce Ovbiagele, associate dean and professor of neurology at the University of California, San Francisco (UCSF), has been honored with the prestigious W. Lester Henry Award for Diversity, Equity, and Inclusion by the American College of Physicians. This esteemed accolade is a testament to Dr. Ovbiagele's unwavering commitment to fostering diversity and equity within the healthcare landscape.
Faculty members discuss impact of AI on academic research
Elizabeth Kivowitz | ekivowitz@stratcomm.ucla.edu | UCLA Newsroom
More than 150 UCLA faculty, staff, postdocs, graduate and undergraduate students attended or tuned in to the livestream of Research in the Age of AI Symposium, which was held Feb. 15 at the California NanoSystems Institute at UCLA.
Professor Ken Lange honored at annual symposium
UCLA Receives $4.6M Grant from The Warren Alpert Foundation to Launch Computational Biology/AI Training Program
UCLA has received a $4.6 million grant from The Warren Alpert Foundation to establish the Warren Alpert UCLA Computational Biology/AI Training and Retention Program.
UCLA SwabSeq Lab Completes 2 Million COVID-19 Diagnostic Tests
More than four years after the world first learned about COVID that led to an unprecedented global health crisis in modern history and upended life as we knew it, UCLA researchers behind the SwabSeq COVID-19 PCR test came together November 13 in honor of SwabSeq’s third anniversary and its milestone of reaching 2 million processed tests.
Deep learning-based phenotype imputation on population-scale biobank data increases genetic discoveries
Biobanks that collect deep phenotypic and genomic data across many individuals have emerged as a key resource in human genetics. However, phenotypes in biobanks are often missing across many individuals, limiting their utility. We propose AutoComplete, a deep learning-based imputation method to impute or ‘fill-in’ missing phenotypes in population-scale biobank datasets. When applied to collections of phenotypes measured across ~300,000 individuals from the UK Biobank, AutoComplete substantially improved imputation accuracy over existing methods.
Biomedical Data Science for Precision Health Equity Trainees Attend National Conference
Three PhD students supported by the Biomedical Data Science for Precision Health Equity training program, along with PI Professor Bogdan Pasaniuc (Computational Medicine) and Professor Alex Bui (Radiological Sciences, Bioengineering), attended the NLM T15 Training Conference, held at Stanford in June. All T-15 institutions from across the country participated in the three-day meeting, which featured a keynote address from Dr.
UCLA biobank study reveals disease risk, health care use among LA’s diverse population
A new study of UCLA Health’s large genetic biobank is giving researchers new insights into the disease risks faced by the region’s diverse communities and their access to health care. The effort, published in Nature Medicine, may prove useful in developing personalized medicine and treatment approaches to groups often overlooked by the medical system.