- Pietro Barbiero7,
- Andrea Bertotti8,
- Gabriele Ciravegna9,
- Giansalvo Cirrincione10,11,
- Elio Piccolo12 &
- …
- Alberto Tonda13
Part of the book series:Smart Innovation, Systems and Technologies ((SIST,volume 151))
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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.
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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Authors and Affiliations
Department of Mathematical Sciences, Politecnico di Torino, Turin, Italy
Pietro Barbiero
Dipartimento di Oncologia, Candiolo Cancer Institute - FPO, Università degli studi di Torino, Torino, Italy
Andrea Bertotti
Università degli Studi di Siena, DIISM, Siena, Italy
Gabriele Ciravegna
Lab. LTI, University of Picardie Jules Verne, Amiens, France
Giansalvo Cirrincione
University of South Pacific, Suva, Fiji
Giansalvo Cirrincione
Politecnico di Torino, DAUIN, Turin, Italy
Elio Piccolo
UMR 782, Université Paris-Saclay, INRA, Thiverval-Grignon, France
Alberto Tonda
- Pietro Barbiero
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- Andrea Bertotti
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- Gabriele Ciravegna
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- Giansalvo Cirrincione
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- Elio Piccolo
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- Alberto Tonda
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Corresponding author
Correspondence toPietro Barbiero.
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Editors and Affiliations
Department of Psychology, University of Campania Luigi Vanvitelli, Caserta, Italy
Anna Esposito
Tecnocampus, Mataró, Spain
Marcos Faundez-Zanuy
Department of Civil, Environment, Energy and Materials Engineering, Mediterranea University of Reggio Calabria, Reggio Calabria, Italy
Francesco Carlo Morabito
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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