Título: Feature Selection via Genetic Algorithms in the Classification of Anti-Snake Venom Medicinal Plants
Autores: Oliveira, Lariza L. de; Persinoti, Gabriela F.; Giuliatti, Silvana; Tinós, Renato
Resumo: In this work, Genetic Algorithm (GA) is employed in feature selection for the classification of medicinal plants with snake venom-neutralizing properties. The classification is performed using an Artificial Neural Network (ANN), which indicates the medicinal plants with anti-snake venom action as output when an amino acid sequence of snake venom is presented in its input. GAs and ANNs are Artificial Intelligence techniques and have been used in several similar optimization and classification problems. Here, the feature selection system is implemented using the classification error rate of the training set and the number of attributes as the fitness of each individual of the GA. The validation results for the classification system indicate that ANNs can be used to aid the selection of medicinal plants with snake venom-neutralizing properties. Also, feature selection based on GAs can help researches to select amino acids sequences of the snake venoms which can be important to the interaction with medicinal plants compounds.
Palavras-chave: Bioinformatics; Genetic Algorithms; Artificial Neural Networks; Artificial Intelligence; Snake venom; Medicinal plants
Páginas: 10
Código DOI: 10.21528/lmln-vol8-no3-art1
Artigo em PDF: vol8-no3-art1.pdf
Arquivo BibTex: vol8-no3-art1.bib