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Machine Learning for Health Informatics pp 415–434Cite as

Machine Learning for In Silico Modeling of Tumor Growth

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9605))

Abstract

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.

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Authors and Affiliations

  1. Holzinger Group HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz, Austria

    Fleur Jeanquartier, Claire Jean-Quartier & Andreas Holzinger

  2. Princess Margaret Cancer Centre, University Health Network, Toronto, Canada

    Max Kotlyar, Tomas Tokar, Anne-Christin Hauschild & Igor Jurisica

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  1. Fleur Jeanquartier
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  2. Claire Jean-Quartier
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  7. Andreas Holzinger
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Correspondence to Fleur Jeanquartier .

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    Andreas Holzinger

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Jeanquartier, F. et al. (2016). Machine Learning for In Silico Modeling of Tumor Growth. In: Holzinger, A. (eds) Machine Learning for Health Informatics. Lecture Notes in Computer Science(), vol 9605. Springer, Cham. https://doi.org/10.1007/978-3-319-50478-0_21

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