DNA Microarray Classification: Evolutionary Optimization of Neural Network Hyper-parameters

Pietro Barbiero (1) , Andrea Bertotti , Gabriele Ciravegna (2, 3) , Giansalvo Cirrincione (4) , Elio Piccolo

1 CAM - University of Cambridge [UK]
2 UniCA - Université Côte d'Azur
3 UniFI - Università degli Studi di Firenze = University of Florence = Université de Florence
4 LTI - Laboratoire des technologies innovantes - UR UPJV 3899

The analysis of complex systems, such as cancer resistance to drugs, requires flexible algorithms but also simple models, as they will be used by biologists in order to get insights on the underlying phenomenon. Exploiting the availability of the largest collection of patient-derived xenografts from metastatic colorectal cancer annotated for response to therapies, this manuscript aims to (i) forecast the response to treatments on human tissues using murine information; (ii) providing a trade-off between model accuracy and interpretability, evolving a shallow neural network using a genetic algorithm.

Domaines: Sciences de l'ingénieur [physics]

Pietro Barbiero, Andrea Bertotti, Gabriele Ciravegna, Giansalvo Cirrincione, Elio Piccolo. DNA Microarray Classification: Evolutionary Optimization of Neural Network Hyper-parameters. Esposito, A and FaundezZanuy, M and Morabito, FC and Pasero, E. NEURAL APPROACHES TO DYNAMICS OF SIGNAL EXCHANGES, 151, pp.305-311, 2020, Smart Innovation, Systems and Technologies, 978-981-13-8950-4; 978-981-13-8949-8. ⟨10.1007/978-981-13-8950-4\_28⟩. ⟨hal-03631433⟩