Uso e desenvolvimento de métodos computacionais para solucionar problemas biológicos

Abstract

The explosion of genomic data in recent decades has presented a substantial challenge, demanding new approaches to study for efficient analysis and interpretation. This research emerges in this context, offering a comprehensive analysis in bioinformatics, exploring various facets of genomics and its relevance to health. The study encompasses the development of support tools for the analysis of mitochondrial genomes of Amazonian species, the investigation of genetic variants and their correlation with the survival of gastric cancer patients in Natal-RN, and the development of the DTreePred application, designed to predict the pathogenicity of these variants. Additionally, the results of the analysis of gastric cancer patients in Belém-PA are discussed, employing machine learning for disease detection based on genetic variants. To validate the AI models developed based on the population from Pará, public samples of Korean patients with and without gastric cancer were used. It is noteworthy that the most effective models achieved an accuracy exceeding 90% in classifying Korean patients as normal or cancer carriers. This research thus highlights the productive integration of bioinformatics techniques in genomic research and the understanding of complex diseases, representing significant advancements in the fields of health and genomics.