About this Research Topic
This Research Topic aims to explore the potential of high-order correlation mining in medical applications, with a focus on developing novel methodologies and applications that can effectively handle the complexity and diversity of healthcare data. The goal is to address specific questions such as how to identify and analyze high-order correlations in complex medical datasets, and how these correlations can be applied to improve patient care, disease prevention, and health outcomes. By testing hypotheses related to the integration of electronic health records and imaging data, as well as the development of advanced computational frameworks, this research seeks to enhance the interpretability and applicability of high-order correlation findings in clinical settings.
To gather further insights in the field of high-order correlation mining in medical applications, we welcome articles addressing, but not limited to, the following themes:
- Novel methodologies for identifying and analyzing high-order correlations in complex medical datasets.
- Applications of high-order correlation mining in genomics, proteomics, and other omics technologies.
- Hypergraph-based High-Order Correlation Learning for Medical Applications.
- Integration of electronic health records (EHR) and imaging data for comprehensive disease modeling.
- Advances in computational frameworks and algorithms to handle large-scale health data.
- Case studies demonstrating the impact of high-order correlation analyses on patient care, disease prevention, and health outcomes.
- Ethical considerations and best practices in the use of sensitive health information for data mining purposes.
Keywords: Correlation Mining, Medical Applications, Omics, Computational, Prevention
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.