Machine Learning for In Silico Modeling of Tumor Growth

  • Fleur Jeanquartier
  • Claire Jean-Quartier
  • Max Kotlyar
  • Tomas Tokar
  • Anne-Christin Hauschild
  • Igor Jurisica
  • Andreas Holzinger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9605)


The various interplaying variables of tumor growth remain key questions in cancer research, in particular what makes such a growth malignant and what are possible therapies to stop the growth and prevent re-growth. Given the complexity and heterogeneity of the disease, as well as the steadily growing set of publicly available big data sets, there is an urgent need for approaches to make sense out of these open data sets. Machine learning methods for tumor growth profiles and model validation can be of great help here, particularly, discrete multi-agent approaches.

In this paper we provide an overview of current machine learning approaches used for cancer research with the main focus of highlighting the necessity of in silico tumor growth modeling.


Tumor growth Cancer modeling Machine learning Computational biology 


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Fleur Jeanquartier
    • 1
  • Claire Jean-Quartier
    • 1
  • Max Kotlyar
    • 2
  • Tomas Tokar
    • 2
  • Anne-Christin Hauschild
    • 2
  • Igor Jurisica
    • 2
  • Andreas Holzinger
    • 1
  1. 1.Holzinger Group HCI-KDD, Institute for Medical Informatics, Statistics and DocumentationMedical University GrazGrazAustria
  2. 2.Princess Margaret Cancer CentreUniversity Health NetworkTorontoCanada

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