- author = "Jose Eduardo H. {da Silva} and Heder S. Bernardino and Helio J. C. Barbosa and Alex B. Vieira and Luciana C. D. Campos and Itamar L. {de Oliveira}",
- booktitle = "2020 IEEE Congress on Evolutionary Computation (CEC)",
- title = "Inferring Gene Regulatory Network Models from Time-Series Data Using Metaheuristics",
- year = "2020",
- editor = "Yaochu Jin",
- month = "19-24 " # jul,
- keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Biological system modeling, Data models, Numerical models, Computational modeling, Mathematical model, Integrated circuit modeling",
- isbn13 = "978-1-7281-6929-3",
-
DOI = "
doi:10.1109/CEC48606.2020.9185572",
- abstract = "The inference of Gene Regulatory Networks (GRNs) from gene expression data is a hard and widely addressed scientific challenge with potential industrial and health-care use. Discrete and continuous models of GRNs are often used (i) to understand the process, and (ii) to predict the values of the relevant variables. Here, we propose a procedure to infer models of GRNs from data where (i) the data is binarised, (ii) a Boolean model is created using a Cartesian Genetic Programming technique, (iii) the obtained Boolean model is converted to a system of ordinary differential equations, and (iv) an Evolution Strategy defines the parameters of the continuous model. As a result, we expect to reduce the effect of noise and to improve biological interpretability. The proposed method is applied to two ODE systems that describe the circadian rhythm network dynamic, with 5 and 10 state variables. The models created by the proposed procedure are able to reproduce the behavior observed in the original data.",
-
notes = "Universidade Federal de Juiz de Fora, Juiz de Fora,
Brazil.
Also known as \cite{9185572}",
