Research Topic

Integration of Multisource Heterogenous Omics Information in Cancer

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About this Research Topic

Multisource heterogenous omics data can provide unprecedented perspectives and insights into cancer studies, but also pose great analytical problems for researchers due to the vast amount of data produced. This Research Topic aims to provide a forum for sharing ideas, tools and results among researchers from ...

Multisource heterogenous omics data can provide unprecedented perspectives and insights into cancer studies, but also pose great analytical problems for researchers due to the vast amount of data produced. This Research Topic aims to provide a forum for sharing ideas, tools and results among researchers from various computational cancer biology fields such as genetic/epigenetic and genome-wide studies. We would like to mainly focus on recent progress in heterogenous cancer omics data integration, and analysis approaches to systematically processing multi-omics information.

We sincerely welcome the submission of Review articles, Original Research papers and Methods papers and Software work presenting advances in the cancer genetics, epigenetics, or genome-wide studies, in the context of computational integration and systematic analysis of omics data on the following topics:
• Biomarker discovery, biomedical databases and data integration;
• Comparative and computational epigenomics;
• Deep learning and machine learning in cancer biology;
• Network biology in cancer;
• Data mining and visualization;
• Software tools and applications.

Any wonderful works related to computational cancer biology, but not limited to the above topics, are also very welcome.


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.

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