miRQuest 2: solução computacional para integração de ferramentas de predição de micro RNA utilizando balanceamento de carga

• Ano de defesa: 2019
• Autor(a) principal: Gurka Júnior, Márcio José
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Ponta Grossa; Brasil; Programa de Pós-Graduação em Ciência da Computação; UTFPR
• Palavras-chave em Português: Bioinformática; Ácidos nucleicos; Balanceamento de máquinas; Inteligência computacional; Bioinformatics; Nucleic acids; Balancing of machinery; Computational intelligence; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO; Engenharia/Tecnologia/Gestão
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/4634

Bioinformatics is a field of study that has the need of making tools that are process optimized and have a quick response time for the user. The data to study mainly comes from the group of genetic chains, and from all the types of chains, the micro RNA one is the chosen class to study and because of that, the miRNA predictions tools Mirinho and miRBoost were picked to be integrated in a single computational solution. The solutions has been named miRQuest 2 and the prediction tool integration has been made by load balancing using the Round Robin algorithm, also the Python programming language has been used integrated with an React interface has been developed to process a FASTA file provided by miRBase. The algorithm provide optimizations and the response time has been considered satisfactory. The difference between the Mirinho execution time at the command line and the computational solution was 20%, while the miRBoost tool was 5%.