About this Research Topic
This Research Topic aims to explore the synergy between multi-omics data analysis and AI in the context of cancer research. We seek to address the following questions: How can multi-omics data be effectively integrated to enhance our understanding of cancer biology? How can AI algorithms and techniques be applied to analyze and interpret these vast datasets? What recent advances have been made in this interdisciplinary field, and how can they be translated into practical applications for cancer prevention and treatment?
We invite contributions that span a wide range of themes within this Research Topic, including but not limited to:
• Methods for integrating multi-omics data in cancer research.
• AI-driven predictive models for cancer risk assessment and early detection.
• Identification of novel biomarkers and therapeutic targets through multi-omics analysis.
• Applications of machine learning and deep learning in cancer genomics.
We welcome original research articles and reviews that elucidate the intersection of multi-omics data analysis and artificial intelligence in the context of cancer prevention and treatment. By fostering collaboration between computational genomics and AI experts, this Research Topic aims to accelerate progress towards more effective cancer management strategies.
Keywords: cancer, multi-omics, computational genomics, artificial intelligence
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.