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.
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.
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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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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