Abstract
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.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Hidalgo, M., et al.: Patient-derived Xenograft models: An emerging platform for translational cancer research. Cancer Discov. 4, 998–1013 (2014)
Tentler, J.J., et al.: Patient-derived tumour xenografts as models for oncology drug development. Nat. Rev. Clin. Oncol. 9, 338–350 (2012)
Byrne, A.T., et al.: Interrogating open issues in cancer precision medicine with patient derived xenografts. Nat. Rev. Cancer (2017). https://doi.org/10.1038/nrc.2016.140
Bertotti, A., et al.: A molecularly annotated platform of patient-derived xenografts (‘xenopatients’) identifies HER2 as an effective therapeutic target in cetuximab-resistant colorectal cancer. Cancer Discov. 1, 508–523 (2011)
Zanella, E.R., et al.: IGF2 is an actionable target that identifies a distinct subpopulation of colorectal cancer patients with marginal response to anti-EGFR therapies. Sci. Transl. Med. 7, (2015)
Bertotti, A., et al.: The genomic landscape of response to EGFR blockade in colorectal cancer. Nature 526, 263–7 (2015)
Sartore Bianchi, A. et al., Dual-targeted therapy with trastuzumab and lapatinib in treatment-refractory, KRAS codon 12/13 wild-type, HER2-positive metastatic colorectal cancer (HERACLES): a proof-of-concept, multicentre, open-label, phase 2 trial. Lancet Oncol. 17, 738–746 (2016)
Barbiero P., Bertotti A., Ciravegna G., Cirrincione G., Pasero E., Piccolo E.: Supervised gene identification in colorectal cancer. In: Quantifying and Processing Biomedical and Behavioral Signals. Springer (2018). ISBN 9783319950945. https://doi.org/10.1007/978-3-319-95095-2_21
Illumina: Array-based gene expression analysis. Data Sheet Gene Expr. (2011). http://res.illumina.com/documents/products/datasheets/datasheet_gene_exp_analysis.pdf
Isella, C., et al.: Selective analysis of cancer-cell intrinsic transcriptional traits defines novel clinically relevant subtypes of colorectal cancer. Nat. Gen. 8, (2017). https://doi.org/10.1038/ncomms15107
Bevilacqua, V., Mastronardi, G., Menolascina, F.: Genetic algorithm and neural network based classification in microarray data analysis with biological validity assessment. In: International Conference on Intelligent Computing, pp. 475–484. Springer (2006)
Widrow, B., Lehr, M.A.: Artificial Neural Networks of the perceptron, madaline, and backpropagation family. In: Neurobionics (1993). https://doi.org/10.1016/B978-0-444-89958-3.50013-9
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall (1998). ISBN 0132733501
Chollet, F., et al.: Keras (2015). https://keras.io
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference for Learning Representations (2017). arXiv:1412.6980v9
Michalewicz, Z., Hartley, S.J.: Genetic algorithms + data structures = evolution programs. Math. Intell. 18(3), 71 (1996)
Garrett, A.: Inspyred: bio-inspired algorithms in python (2014). https://pypi.python.org/pypi/inspyred (visited on 11/28/2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Barbiero, P., Bertotti, A., Ciravegna, G., Cirrincione, G., Piccolo, E. (2020). DNA Microarray Classification: Evolutionary Optimization of Neural Network Hyper-parameters. 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_28
Download citation
DOI: https://doi.org/10.1007/978-981-13-8950-4_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-8949-8
Online ISBN: 978-981-13-8950-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)