Integrative analysis and machine learning on cancer genomics data using the Cancer Systems Biology Database (CancerSysDB).

Journal: BMC bioinformatics
Published Date: Apr 24, 2018

Abstract

BACKGROUND: Recent cancer genome studies on many human cancer types have relied on multiple molecular high-throughput technologies. Given the vast amount of data that has been generated, there are surprisingly few databases which facilitate access to these data and make them available for flexible analysis queries in the broad research community. If used in their entirety and provided at a high structural level, these data can be directed into constantly increasing databases which bear an enormous potential to serve as a basis for machine learning technologies with the goal to support research and healthcare with predictions of clinically relevant traits.

Authors

  • Rasmus Krempel
    Regional Computing Center of the University of Cologne (RRZK), Cologne, Germany.
  • Pranav Kulkarni
    Bioinformatics Facility, CECAD Research Center, University of Cologne, Cologne, Germany.
  • Annie Yim
    Institut de Biologie du Développement, Aix-Marseille University, Marseille, France.
  • Ulrich Lang
    Regional Computing Center of the University of Cologne (RRZK), Cologne, Germany.
  • Bianca Habermann
    Institut de Biologie du Développement, Aix-Marseille University, Marseille, France.
  • Peter Frommolt
    Bioinformatics Facility, CECAD Research Center, University of Cologne, Cologne, Germany. peter.frommolt@uni-koeln.de.

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