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Evolutionary Pattern Recognition in Complex Biological Networks

Grant number: 18/00147-5
Support type:Scholarships abroad - Research Internship - Doctorate
Effective date (Start): April 01, 2018
Effective date (End): September 30, 2018
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal Investigator:Odemir Martinez Bruno
Grantee:Gisele Helena Barboni Miranda
Supervisor abroad: Bernard de Baets
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Local de pesquisa : Ghent University (UGent), Belgium  
Associated to the scholarship:15/05899-7 - Pattern recognition in complex networks through automata, BP.DR

Abstract

Many biological systems can be represented as complex networks. In contrast to established approaches that analyze these networks on the basis of their structural properties, they can also be studied by investigating the patterns that are evolved by a discrete dynamical system built upon these networks. Among the computational and mathematical tools for the study of complex systems are cellular automata. Combined to networks these tools can be used to map the relationship between the network architecture and their consequent impacts on the patterns evolved by the governing spatially discrete dynamical system. The aim of this proposal is to develop evolutionary methods for the characterization of network topology, thereby giving particular attention to topologies that are common among biological networks. More precisely, the evolved spatio-temporal patterns will be used to characterize different network topologies in order to be able to perform classification tasks in the context of pattern recognition. Several biological networks are being considered throughout the course of the project in order to gather expertise on evolutionary network analysis in several domains, such as the spatio-temporal spread of influenza in Belgium and fungal networks that account for the connections between individual hyphae that can be derived from fungal imagery. Therefore, we aim to contribute with biological network characterization through the study and the analysis of the concerned biological systems, represented as networks, through the use of the proposed network descriptors.

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
MIRANDA, GISELE H. B.; BAETENS, JAN M.; BOSSUYT, NATHALIE; BRUNO, ODEMIR M.; DE BAETS, BERNARD. Real-time prediction of influenza outbreaks in Belgium. EPIDEMICS, v. 28, SEP 2019. Web of Science Citations: 0.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.