Navegando por Autor "OLIVEIRA, Ewerton Cristhian Lima de"

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Acesso aberto (Open Access)
Development of machine learning-based frameworks to predict permeability of peptides through cell membrane and blood-brain barrier
(Universidade Federal do Pará, 2024-03-27) OLIVEIRA, Ewerton Cristhian Lima de; LIMA, Anderson Henrique Lima e; http://lattes.cnpq.br/2589872959709848; https://orcid.org/0000-0002-8451-9912; SALES JUNIOR, Claudomiro de Souza de; http://lattes.cnpq.br/4742268936279649
Peptides comprise a versatile class of biomolecules with diverse physicochemical and structural properties, in addition to numerous pharmacological and biotechnological applications. Some groups of peptides can cross biological membranes, such as the cell membrane and the human blood-brain barrier. Researchers have explored this property over the years as an alternative to developing more powerful drugs, given that some peptides can also be drug carriers. Although some machine learning-based tools have been developed to predict cell-penetrating peptides (CPPs) and blood-brain barrier penetrating peptides (B3PPs), some points have not yet been explored within this theme. These points encompass the use of dimensionality reduction (DR) techniques in the preprocessing stage, molecular descriptors related to drug bioavailability, and data structures that encode peptides with chemical modifications. Therefore, the primary purpose of this thesis is to develop and test two frameworks based on DR, the first one to predict CPPs and the second to predict B3PPs, also evaluating the molecular descriptors and data structure of interest. The results of this thesis show that for the prediction of penetration in the cell membrane, the proposed framework reached 92% accuracy in the best performance in an independent test, outperforming other tools created for the same purpose, besides evidencing the contribution between the junction of molecular descriptors based on amino acid sequence and those related to bioavailability and cited in Lipinski’s rule of five. Furthermore, the prediction of B3PPs by the proposed framework reveals that the best model using structural, electric, and bioavailability-associated molecular descriptors achieved average accuracy values exceeding 93% in the 10-fold cross-validation and between 75% and 90% accuracy in the independent test for all simulations, outperforming other machine learning (ML) tools developed to predict B3PPs. These results show that the proposed frameworks can be used as an additional tool in predicting the penetration of peptides in these two biomembranes and are available as free-touse web servers.
Acesso aberto (Open Access)
Proposta de um framework para identificação de sistemas dinâmicos multivariáveis não lineares
(Universidade Federal do Pará, 2020-02-27) OLIVEIRA, Ewerton Cristhian Lima de; ARAÚJO, Jasmine Priscyla Leite de; http://lattes.cnpq.br/4001747699670004
The techniques of dynamic systems identification are algorithms of most importance for generating mathematical and computational models capable to represent the dynamic of systems and processes present in many fields of society, such as: industrial processes; automobiles; food production; aerospace vehicles; biological systems and etc. The identification of these systems, which generally have more than one variable of input and output (multivariable systems) and also are nonlinear, it is very important for science and engineering in relation to the development of new control techniques, fault monitoring and prediction of operating state of these mechanisms. Nonetheless, the identification of nonlinear MIMO (Multiple Input Multiple Output) systems is a hard task, as much due the difficulty of implementing the classic algorithms for solve this problem, as the fact that nonlinear systems require complex models for represent their dynamics in satisfactory way. In order to contribute with the solution of this problem, this work proposes a framework capable of performing as much the identification of nonlinear dynamic MIMO systems in multivariable fuzzy TSK model, which can represent in simple way the coupling among the variables involved in identification, as the selection of regressor vector used in model. To perform fuzzy TSK multivariable model parameterization, the proposed framework uses the algorithms Least Square (LS) and Particle Swarm Optimization (PSO), which are responsible to estimate the matrix of parameters and the set of standard deviation of the Gaussians in model inputs, respectively. The proposed methodology is tested and compared with RNA and a Hammerstein-Wiener (WH) model in identification of two nonlinear MIMO industrial plants: Continuous Stirred Tank Reactor (CSTR); Industrial Dryer. The comparison of these three techniques is made with base in indices of Mean Squared Error (𝑀𝑆𝐸) and Variance Accounted For (𝑉𝐴𝐹), further the analysis of residues between the observed and estimated data. The results show that the proposed framework got the best performance, based in the two indices, in 80% of outputs estimation of the two multivariable plants, and also reached the best performance in 60% of residual analysis of plants identification.