Multi – vs. single – perceptron approach for modelling the pattern recognition and classification in a multi-compartment adaptive immune system model

Scheidegger, Stephan; Füchslin, Rudolf M.; Gaipl, Udo S.

doi:10.1162/isal_a_00574

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-30886

Publication type:	Conference paper
Type of review:	Peer review (publication)
Title:	Multi – vs. single – perceptron approach for modelling the pattern recognition and classification in a multi-compartment adaptive immune system model
Authors:	Scheidegger, Stephan Füchslin, Rudolf M. Gaipl, Udo S.
et. al:	No
DOI:	10.1162/isal_a_00574 10.21256/zhaw-30886
Proceedings:	ALIFE 2023: Ghost in the Machine : Proceedings of the 2023 Artificial Life Conference
Page(s):	7
Conference details:	International Conference on Artificial Life (ALIFE), Sapporo, Japan, 24-28 July 2023
Issue Date:	24-Jul-2023
Publisher / Ed. Institution:	MIT Press
Publisher / Ed. Institution:	Cambridge
Language:	English
Subjects:	Biological neural network; Immune tumour host ecosystem
Subject (DDC):	006: Special computer methods 616: Internal medicine and diseases
Abstract:	The computer simulation of tumor – host ecosystems interacting with an adaptive immune system may serve as a tool for anti-cancer treatment optimization, but requires appropriate mathematical models. Regarding the tasks of the adaptive immune system (antigen pattern recognition and classification), a perceptron can be used as a conceptual structure representing corresponding biological structures for antigen pattern recognition and classification such as Antigen Presenting Cells (APC’s) and their interaction with effector cells in lymph nodes. Regarding the topology of the lymph vessel network, the adaptive immune system may be represented by several perceptrons receiving information about antigen patterns from different tissue compartments. In this study, two scenarios of lymph node arrangement have been investigated. In both scenarios, a tumor-host tissue compartment is treated with ionizing radiation and a second compartment with host tissue and a tumor metastasis is not irradiated. The results exhibit a dependence of the immune response onto the lymph node arrangement, indicating that the topology of the lymph node network is important for an optimal adaptive immune response. The presented simplistic model structure does not allow for a perfect classification between tumor and host tissue. Instead of a single perceptron which is related to the interaction of immune cells in a corresponding lymph node as suggested in this study, networks of locally interacting units may be considered as layers building a deep (convolutional) neural network - like structure.
URI:	https://digitalcollection.zhaw.ch/handle/11475/30886
Fulltext version:	Published version
License (according to publishing contract):	CC BY 4.0: Attribution 4.0 International
Departement:	School of Engineering
Organisational Unit:	Institute of Applied Mathematics and Physics (IAMP)
Appears in collections:	Publikationen School of Engineering

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Scheidegger, S., Füchslin, R. M., & Gaipl, U. S. (2023). Multi – vs. single – perceptron approach for modelling the pattern recognition and classification in a multi-compartment adaptive immune system model [Conference paper]. ALIFE 2023: Ghost in the Machine : Proceedings of the 2023 Artificial Life Conference, 7. https://doi.org/10.1162/isal_a_00574

Scheidegger, S., Füchslin, R.M. and Gaipl, U.S. (2023) ‘Multi – vs. single – perceptron approach for modelling the pattern recognition and classification in a multi-compartment adaptive immune system model’, in ALIFE 2023: Ghost in the Machine : Proceedings of the 2023 Artificial Life Conference. Cambridge: MIT Press, p. 7. Available at: https://doi.org/10.1162/isal_a_00574.

S. Scheidegger, R. M. Füchslin, and U. S. Gaipl, “Multi – vs. single – perceptron approach for modelling the pattern recognition and classification in a multi-compartment adaptive immune system model,” in ALIFE 2023: Ghost in the Machine : Proceedings of the 2023 Artificial Life Conference, Jul. 2023, p. 7. doi: 10.1162/isal_a_00574.

SCHEIDEGGER, Stephan, Rudolf M. FÜCHSLIN und Udo S. GAIPL, 2023. Multi – vs. single – perceptron approach for modelling the pattern recognition and classification in a multi-compartment adaptive immune system model. In: ALIFE 2023: Ghost in the Machine : Proceedings of the 2023 Artificial Life Conference. Conference paper. Cambridge: MIT Press. 24 Juli 2023. S. 7

Scheidegger, Stephan, Rudolf M. Füchslin, and Udo S. Gaipl. 2023. “Multi – vs. Single – Perceptron Approach for Modelling the Pattern Recognition and Classification in a Multi-Compartment Adaptive Immune System Model.” Conference paper. In ALIFE 2023: Ghost in the Machine : Proceedings of the 2023 Artificial Life Conference, 7. Cambridge: MIT Press. https://doi.org/10.1162/isal_a_00574.

Scheidegger, Stephan, et al. “Multi – vs. Single – Perceptron Approach for Modelling the Pattern Recognition and Classification in a Multi-Compartment Adaptive Immune System Model.” ALIFE 2023: Ghost in the Machine : Proceedings of the 2023 Artificial Life Conference, MIT Press, 2023, p. 7, https://doi.org/10.1162/isal_a_00574.