Integration of Multisource Heterogenous Omics Information in Cancer

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Victor Jin

The University of Texas Health Science Center at San Antonio

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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.

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Editors

Victor Jin

The University of Texas Health Science Center at San Antonio

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149,201 Total views

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Published In

Frontiers in Genetics

Computational Genomics

Frontiers in Plant Science

Computational Genomics

Frontiers in Bioengineering and Biotechnology

Computational Genomics

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