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Understanding Cancer Phenomenon at Gene Expression Level by using a Shallow Neural Network Chain

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Abstract

Exploiting the availability of the largest collection of patient-derived xenografts from metastatic colorectal cancer annotated for a response to therapies, this manuscript aims to characterize the biological phenomenon from a mathematical point of view. In particular, we design an experiment in order to investigate how genes interact with each other. By using a shallow neural network model, we find reduced feature subspaces where the resistance phenomenon may be much easier to understand and analyze.

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Notes

  1. 1.

    The described shallow neural network model is equivalent to a linear regression model with an L2 regularization of the parameters also known as ridge regression [16].

  2. 2.

    The corresponding standard deviation is always in the order of few percentage decimals, and it is not directly displayed since it is not relevant to the purpose of the discussion. However, you can reproduce the experiment by using our code if you need more precision.

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Author information

Authors and Affiliations

  1. Department of Mathematical Sciences, Politecnico di Torino, Turin, Italy

    Pietro Barbiero

  2. Dipartimento di Oncologia, Candiolo Cancer Institute - FPO, Università degli studi di Torino, Torino, Italy

    Andrea Bertotti

  3. Università degli Studi di Siena, DIISM, Siena, Italy

    Gabriele Ciravegna

  4. Lab. LTI, University of Picardie Jules Verne, Amiens, France

    Giansalvo Cirrincione

  5. University of South Pacific, Suva, Fiji

    Giansalvo Cirrincione

  6. Politecnico di Torino, DAUIN, Turin, Italy

    Elio Piccolo

  7. UMR 782, Université Paris-Saclay, INRA, Thiverval-Grignon, France

    Alberto Tonda

Authors
  1. Pietro Barbiero

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  2. Andrea Bertotti

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  3. Gabriele Ciravegna

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  4. Giansalvo Cirrincione

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  5. Elio Piccolo

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  6. Alberto Tonda

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Corresponding author

Correspondence toPietro Barbiero.

Editor information

Editors and Affiliations

  1. Department of Psychology, University of Campania Luigi Vanvitelli, Caserta, Italy

    Anna Esposito

  2. Tecnocampus, Mataró, Spain

    Marcos Faundez-Zanuy

  3. Department of Civil, Environment, Energy and Materials Engineering, Mediterranea University of Reggio Calabria, Reggio Calabria, Italy

    Francesco Carlo Morabito

  4. Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Turin, Italy

    Eros Pasero

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Barbiero, P., Bertotti, A., Ciravegna, G., Cirrincione, G., Piccolo, E., Tonda, A. (2020). Understanding Cancer Phenomenon at Gene Expression Level by using a Shallow Neural Network Chain. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Neural Approaches to Dynamics of Signal Exchanges. Smart Innovation, Systems and Technologies, vol 151. Springer, Singapore. https://doi.org/10.1007/978-981-13-8950-4_26

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