A5028441941- Biomedical and Engineering Education
- Biomedical Text Mining and Ontologies
- Artificial Intelligence in Healthcare and Education
- scientometrics and bibliometrics research
- Scientific Computing and Data Management
- Research Data Management Practices
- Genetics, Bioinformatics, and Biomedical Research
- Mental Health Research Topics
- Interdisciplinary Research and Collaboration
University of Alabama at Birmingham
2023-2024
High-quality biomedical datasets are essential for medical research and disease treatment innovation. The NIH-funded Bridge2AI project strives to facilitate such innovations by uniting top-tier, diverse teams curate designed AI-driven research. We examined 1,699 dataset papers from the Nucleic Acids Research (NAR) database issues Talent Knowledge Graph. By treating each paper's authors as a team, we explored relationship between team attributes (team power fairness) paper quality, measured...
We present an interactive visualization of the Cell Map for AI Talent Knowledge Graph (CM4AI TKG), a detailed semantic space comprising approximately 28,000 experts and 1,000 datasets focused on biomedical field. Our tool leverages transformer-based embeddings, WebGL techniques, generative AI, specifically Large Language Models (LLMs), to provide responsive user-friendly interface. This supports exploration around 29,000 nodes, assisting users in identifying potential collaborators dataset...
The Bridge2AI project, funded by the National Institutes of Health, involves researchers from different disciplines and backgrounds to develop well-curated AI health data tools. Understanding cross-disciplinary cross-organizational collaboration at individual, team, project levels is critical. In this paper, we matched team members PubMed Knowledge dataset get their health-related publications. We built network for all collaborators sorted out with largest degree centrality betweenness...