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Jake Chen, Ph.D., Chief Bioinformatics Officer of the UAB Informatics Institute, served as the session chair of Representational Learning in Genome Biology and Precision Medicine during the Great Lakes Bioinformatics (GLBIO) Conference in Montreal, Quebec, Canada. The conference was held[…]
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Sooryanarayana Varambally, Ph.D., MBA, professor in the Department of Pathology's Division of Molecular and Cellular Pathology and adjunct faculty at the Informatics Institute, was one of the 16 faculty members who were named the winners of the 2023 Dean's Excellence[…]
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The University of Alabama at Birmingham hosted the Service Learning and Undergraduate Research Expo from April 17th through April 21st. Maryam Choucha, a Bioinformatics student, received first place at the 2023 Spring Expo in the Physical and Applied Sciences category,[…]
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The UAB Informatics Institute, along with the Center for Clinical and Translational Sciences (CCTS), hosted the 7th Annual Translational and Transformative Informatics Symposium (ATTIS 2023) on April 21 with the theme of “Artificial Intelligence (AI) in Medicine and Healthcare”. Panel[…]
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The new Department of Biomedical Informatics and Data Sciences will be the 28th academic department in the Heersink School of Medicine.
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Registration is free and open to the public and will close on Friday, April 14.
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These effector memory B cells appear poised for a rapid serum antibody response upon secondary challenge one year later, and evidence shows that the cells in this subset differ from all previously described memory B cell subsets.
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The UAB Department of Biomedical Engineering (BME) graduate program has been the recipient of seven Blazer Fellowships in 2022, and a Bioinformatics student recently etched his name as a part of this notable accomplishment. Samuel Bharti, a college student from[…]
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The UAB projects will investigate was to improve and increase the use of artificial intelligence in biomedical research.
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Owsley and McGwin will lead three data collection sites in the collection of data to inform machine learning approaches to provide critical insights into the endemic condition Type 2 diabetes mellitus.
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