Modelling, Aggregation and Simulation of a Dynamic Biological System through Fuzzy Cognitive Maps

Gonzalo Nápoles; Isel Grau; Maikel León Espinosa; Ricardo Grau

doi:10.1007/978-3-642-37798-3_17

Modelling, Aggregation and Simulation of a Dynamic Biological System through Fuzzy Cognitive Maps

Gonzalo Nápoles, Isel Grau, Maikel León Espinosa, Ricardo Grau

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

15 Citations (Scopus)

Abstract

The complex dynamics of Human Immunodeficiency Virus leads to serious problems on predicting the drug resistance. Several machine learning techniques have been proposed for modelling this classification problem, but most of them are difficult to aggregate and interpret. In fact, in last years the protein modelling of this virus has become, from diverse points of view, an open problem for researchers. This paper presents a modelling of the protease protein as a dynamic system through Fuzzy Cognitive Maps, using the amino acids contact energies for the sequence description. In addition, a learning scheme based on swarm intelligence called PSO-RSVN is used to estimate the causal weight matrix that characterizes these structures. Finally, an aggregation procedure with previously adjusted maps is applied for obtaining a prototype map, in order to discover knowledge in the causal influences, and simulate the system behaviour when a single (or multiple) mutation takes place.

Original language	English
Title of host publication	Advances in Computational Intelligence
Subtitle of host publication	11th Mexican International Conference on Artificial Intelligence, MICAI 2012, San Luis Potosí, Mexico, October 27 – November 4, 2012. Revised Selected Papers, Part II
Publisher	Springer
Pages	188-199
Number of pages	12
ISBN (Print)	978-3-642-37797-6
DOIs	https://doi.org/10.1007/978-3-642-37798-3_17
Publication status	Published - 2012
Externally published	Yes

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	7630

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-642-37798-3_17

Cite this

Nápoles, G., Grau, I., León Espinosa, M., & Grau, R. (2012). Modelling, Aggregation and Simulation of a Dynamic Biological System through Fuzzy Cognitive Maps. In Advances in Computational Intelligence: 11th Mexican International Conference on Artificial Intelligence, MICAI 2012, San Luis Potosí, Mexico, October 27 – November 4, 2012. Revised Selected Papers, Part II (pp. 188-199). (Lecture Notes in Computer Science; Vol. 7630). Springer. https://doi.org/10.1007/978-3-642-37798-3_17

Nápoles, Gonzalo ; Grau, Isel ; León Espinosa, Maikel et al. / Modelling, Aggregation and Simulation of a Dynamic Biological System through Fuzzy Cognitive Maps. Advances in Computational Intelligence: 11th Mexican International Conference on Artificial Intelligence, MICAI 2012, San Luis Potosí, Mexico, October 27 – November 4, 2012. Revised Selected Papers, Part II. Springer, 2012. pp. 188-199 (Lecture Notes in Computer Science).

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title = "Modelling, Aggregation and Simulation of a Dynamic Biological System through Fuzzy Cognitive Maps",

abstract = "The complex dynamics of Human Immunodeficiency Virus leads to serious problems on predicting the drug resistance. Several machine learning techniques have been proposed for modelling this classification problem, but most of them are difficult to aggregate and interpret. In fact, in last years the protein modelling of this virus has become, from diverse points of view, an open problem for researchers. This paper presents a modelling of the protease protein as a dynamic system through Fuzzy Cognitive Maps, using the amino acids contact energies for the sequence description. In addition, a learning scheme based on swarm intelligence called PSO-RSVN is used to estimate the causal weight matrix that characterizes these structures. Finally, an aggregation procedure with previously adjusted maps is applied for obtaining a prototype map, in order to discover knowledge in the causal influences, and simulate the system behaviour when a single (or multiple) mutation takes place.",

author = "Gonzalo N{\'a}poles and Isel Grau and {Le{\'o}n Espinosa}, Maikel and Ricardo Grau",

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language = "English",

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Nápoles, G, Grau, I, León Espinosa, M & Grau, R 2012, Modelling, Aggregation and Simulation of a Dynamic Biological System through Fuzzy Cognitive Maps. in Advances in Computational Intelligence: 11th Mexican International Conference on Artificial Intelligence, MICAI 2012, San Luis Potosí, Mexico, October 27 – November 4, 2012. Revised Selected Papers, Part II. Lecture Notes in Computer Science, vol. 7630, Springer, pp. 188-199. https://doi.org/10.1007/978-3-642-37798-3_17

Modelling, Aggregation and Simulation of a Dynamic Biological System through Fuzzy Cognitive Maps. / Nápoles, Gonzalo; Grau, Isel; León Espinosa, Maikel et al.
Advances in Computational Intelligence: 11th Mexican International Conference on Artificial Intelligence, MICAI 2012, San Luis Potosí, Mexico, October 27 – November 4, 2012. Revised Selected Papers, Part II. Springer, 2012. p. 188-199 (Lecture Notes in Computer Science; Vol. 7630).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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T1 - Modelling, Aggregation and Simulation of a Dynamic Biological System through Fuzzy Cognitive Maps

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AB - The complex dynamics of Human Immunodeficiency Virus leads to serious problems on predicting the drug resistance. Several machine learning techniques have been proposed for modelling this classification problem, but most of them are difficult to aggregate and interpret. In fact, in last years the protein modelling of this virus has become, from diverse points of view, an open problem for researchers. This paper presents a modelling of the protease protein as a dynamic system through Fuzzy Cognitive Maps, using the amino acids contact energies for the sequence description. In addition, a learning scheme based on swarm intelligence called PSO-RSVN is used to estimate the causal weight matrix that characterizes these structures. Finally, an aggregation procedure with previously adjusted maps is applied for obtaining a prototype map, in order to discover knowledge in the causal influences, and simulate the system behaviour when a single (or multiple) mutation takes place.

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Nápoles G, Grau I, León Espinosa M, Grau R. Modelling, Aggregation and Simulation of a Dynamic Biological System through Fuzzy Cognitive Maps. In Advances in Computational Intelligence: 11th Mexican International Conference on Artificial Intelligence, MICAI 2012, San Luis Potosí, Mexico, October 27 – November 4, 2012. Revised Selected Papers, Part II. Springer. 2012. p. 188-199. (Lecture Notes in Computer Science). doi: 10.1007/978-3-642-37798-3_17

Modelling, Aggregation and Simulation of a Dynamic Biological System through Fuzzy Cognitive Maps

Abstract

Publication series

UN SDGs

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