About this Research Topic
The primary challenge in high-order correlation mining within medical applications lies in the complexity and heterogeneity of healthcare data. Medical datasets often include a diverse array of data types, such as genomic information, clinical records, and imaging studies, each presenting unique analytical challenges. Additionally, the sheer volume of data can be overwhelming, necessitating advanced computational techniques to efficiently extract meaningful patterns. Another significant issue is the interpretability of the results; while high-order correlations can provide deep insights, translating these findings into actionable clinical knowledge requires careful consideration and expert validation.
This Research Topic welcomes contributions that explore a broad spectrum of themes related to high-order correlation mining in medical applications, including but not limited to:
• 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.
By addressing these themes, we aim to foster a rich dialogue and collaboration among researchers, clinicians, and data scientists, ultimately contributing to the advancement of medical science and the enhancement of patient care through innovative analytical approaches.
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.