| Texto completo | |
| Autor(es): |
do Amaral, Laurence Rodrigues
;
da Silva Alves, Alexandre Henrick
;
Mendes, Raphael de Lima
;
Gomes, Matheus de Souza
;
Lima Bertarini, Pedro Luiz
;
Hruschka Jr, Estevam Rafael
;
IEEE
Número total de Autores: 7
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021); v. N/A, p. 8-pg., 2021-01-01. |
| Resumo | |
The exponentially growing of generated data and the advanced computing techniques, catalyzed the necessity of innovative strategies to store, analyze and capture biological data. The biocuration was created due to the number and scope of scientific databases in recent years. Biocuration is an essential part of biological and biomedical research discovery. Extracted from the literature, curated data is so important to accomplish computational analysis and to train data mining algorithms. Concurrently, the researchers need a rapidly and more decisive approach to understand unknown domains. In this paper, we propose an architecture to help biocurators in Gene Ontology (GO) classification tasks. Our approach is based on a semi-supervised environment that couples evolutionary computation (i.e. Genetic Algorithms (GA)) and traditional classifiers (i.e. Decision Trees and Naive Bayes). Furthermore, our approach provides high level knowledge (IF-THEN rules and decision trees), balancing accuracy, interpretability and comprehensibility that can help GO biocurators in their classification tasks. Our architecture shows higher classification rates (about 94%, i.e., 17,820 correct samples of 18,959), starting from a small training set, only 2,707 samples (12.5% of 21,666 samples). (AU) | |
| Processo FAPESP: | 13/07787-6 - Modelos e algoritmos para o aprendizado sem fim |
| Beneficiário: | Estevam Rafael Hruschka Júnior |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |