Orchid: a novel management, annotation and machine learning framework for analyzing cancer mutations.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: As whole-genome tumor sequence and biological annotation datasets grow in size, number and content, there is an increasing basic science and clinical need for efficient and accurate data management and analysis software. With the emergence of increasingly sophisticated data stores, execution environments and machine learning algorithms, there is also a need for the integration of functionality across frameworks.

Authors

  • Clinton L Cario
    Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA.
  • John S Witte
    Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA.