Seleção de características utilizando Algoritmo Genético multiobjetivo e k-NN para predição de função de proteína - Santos, Bruno C.; Silberschneider, Cora; Rodrigues, Marcos W.; Nobre, Cristiane N.; Zárate, Luis E. (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe), 2018)
The knowledge of a protein function is essential in many areas, such as bioinformatics, agriculture, and others. Therefore, it is necessary to provide efficient computational models that aim to find the function of a protein. Currently, there is a wealth of available information about protein, such as data from primary, secondary, tertiary and quaternary structures. One of the repositories that provide this information is the Sting DB, which has physicochemical information of the proteins, used by several authors. Our work proposes a methodology using the multiobjective genetic algorithm with non-parametric method k-NN during its genetic evolution, aiming to search the best subset of physical-chemical characteristics for the identification of protein classes. After that, we added new variables and applied PCA to the identified subset, to improve the classification process. In this step, we use the SVM due to its better performance with high dimensionalities data. The proposed methodology demonstrated accuracy values of 72.9% and an f-measure of 68.3%; also we gained about 90% efficiency in processing our approach compared to the previous model, allowing to add new attributes in an attempt to improve the prediction of protein function for future works.