PredictmodelGUI: ferramenta para classificação de genes essenciais através de técnicas de aprendizado de máquina

Abstract

DNA sequencing technologies have provided significant advances in the understanding of the genetic content of numerous organisms, ranging from microorganisms to humans. Among the analyses performed in the Omics Sciences, Annotation stands out as one of the most important. Conceptually, this process consists of inferring biological information from genomic sequences, which allows researchers to understand the function of genetic products, such as Genes — the Basic Units of Heredity responsible for the physical and hereditary characteristics of an organism. Some Genes perform vital functions by encoding Proteins or RNAs essential for processes such as Cellular Metabolism, which participate in crucial pathways like Glycolysis and the Tricarboxylic Acid Cycle. Sequencing Platforms have started to generate large volumes of data, which has driven advances in the Omics fields and fostered the development of computational methods aimed at diverse analyses. More recently, Machine Learning and Artificial Intelligence techniques have been applied to these data, with studies demonstrating the effectiveness of biology-inspired approaches. These models do not require rule-based programming, although their creation still demands advanced skills in Programming and Computing. To contribute toward solving this challenge, this study presents PredictModelGUI, a graphical interface developed in Python that implements nine models to classify Essential Genes. The interface allows importing datasets, re-training models, and adjusting parameters. The information is stored in the software database, which ensures traceability and provides a simple and intuitive tool to test different configurations. Available