Cancer Informatics and Big Data

The availability of large cancer- and health-related data sets offers promise for understanding the genetic basis as well as the mechanisms of the initiation and progression of cancer. However, to optimally use and exploit large databases, optimal informatics and data analytics methods and tools, as well as other resources for developing novel cancer therapies and improving cancer care, must be applied to the sequencing and molecular data. The unavailability of appropriate analytics or prediction tools is often a challenge. The accessible computing power has drastically increased in recent years, requiring further development or replacement of software originally designed for much more limited resources and data. In this section of Cancers, we invite the submission of original research papers as well as timely review articles related to these challenges and opportunities. Topics of interest include, but are not limited to, all areas of cancer informatics and big data analytics, for example:

• Artificial intelligence
• Cancer immunology methods
• Data storage and sharing
• Deep learning
• Drug discovery, drug re-purposing, drug-resistance
• Image processing and analysis
• Next-generation sequencing
• Precision medicine and clinical decision-making
• Single-cell data analytics
• Statistical methods
• Visualization of large data sets