Tumor growth machine learning and understanding

Malignant neoplasms are worldwide one of the leading and increasing causes for death. The underlying complexity of cancer demands for abstractions to disclose an exclusive subset of information related to this disease. AI/Machine learning for tumor growth profiles and model validation may be of great help here. Particularly, discrete multi-agent learning approaches. We are dedicated to support cancer research generally, and to reduce in-vivo experiments specifically, following the 3 R’s: Refine > Reduce > Replace. In this project we are working on the design, development and evaluation of algorithms, methods and tools to support understanding the underlying principles of tumor growth. We follow the assumption that the key to understanding the concepts of cancer lies within an integrative translation and multi-dimensional connection of open data sets.  We dedicate our work in memoriam to the family members and friends we have lost. If we may contribute even very tiny steps to help to save lives in the future our mission was worth our passion, enthusiasm and effort.

  • Recent Publications

    Jean-Quartier, C., Jeanquartier, F. & Holzinger, A. 2020. Open Data for Differential Network Analysis in Glioma. International Journal of Molecular Sciences (SI: Data Analysis and Integration in Cancer Research), 21, (2), 547, doi:10.3390/ijms21020547

    Jeanquartier, F., Jean-Quartier, C. & Holzinger, A. 2019. Visualizing Uncertainty for Comparing Genomic Pediatric Brain Cancer Data. 23rd International Conference Information Visualisation (IV), IEEE, 388-391, doi:10.1109/IV.2019.00072 [RG]

    Jeanquartier, F., Jean-Quartier, C. & Holzinger, A. 2019. Use case driven evaluation of open databases for pediatric cancer research. Springer/Nature BMC BioData Mining, Volume 12, Issue 1, 2, doi:10.1186/s13040-018-0190-8

    Jean-Quartier, C., Jeanquartier, F., Jurisica, I. & Holzinger, A. 2018. In silico cancer research towards 3R. Springer/Nature BMC cancer, 18, (1), 408, doi:10.1186/s12885-018-4302-0.

    Jeanquartier, F., Jean-Quartier, C., Cemernek, D. & Holzinger, A. 2016. In silico modeling for tumor growth visualization. Springer/Nature BMC Systems Biology, 10, (1), 1-15, doi:10.1186/s12918-016-0318-8. Full Open Access > [Springer Link]

    Jeanquartier, F., Jean-Quartier, C., Schreck, T., Cemernek, D. & Holzinger, A. 2016. Integrating Open Data on Cancer in Support to Tumor Growth Analysis. In: Lecture Notes in Computer Science LNCS 9832. Heidelberg, Berlin: Springer, pp. 49-66, doi:10.1007/978-3-319-43949-5_4. [Paper Link] [Springer Link]

    Jean-Quartier, C., Jeanquartier, F., Cemernek, D. & Holzinger, A. 2016. Tumor Growth Simulation Profiling. In: Renda, E. M., Bursa, M., Holzinger, A. & Khuri, S. (eds.) Information Technology in Bio- and Medical Informatics: 7th International Conference, ITBAM 2016, Porto, Portugal, September 5-8, 2016, Proceedings. Heidelberg, Berlin: Springer, pp. 208-213, doi:10.1007/978-3-319-43949-5_16. [Paper Link] [Springer Link]

  • Technical Area

    Machine Learning/AI, Algorithms, Interactive Visualization

  • Application Area

    Biomedical Informatics, Cancer research

  • Keywords

    Tumor-Growth modelling, tumor growth visualization, multi-agents, cellular-potts model, cancer, R3-principle, medical AI

  • Roles

    Project Leader: Andreas HOLZINGER
    Postdoc Researcher: Claire JEAN-QUARTIER
    Postdoc Researcher: Fleur JEANQUARTIER
    More PhDs, Masters and Bachelor students sought
    to support the “in memoriam project”

  • Project period

    The project “in memoriam” was started in 2015