Banca de DEFESA: ROBERTO BRITO XAVIER JUNIOR

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
DISCENTE: ROBERTO BRITO XAVIER JUNIOR
DATA: 14/02/2020
HORA: 10:00
LOCAL: LABCOMP-03 - ICEN
TÍTULO:

Genome Assembly using Reinforcement Learning

PALAVRAS-CHAVES:

Reinforcement learning, Q-learning, Machine learning, Genome assembly.

PÁGINAS: 55
GRANDE ÁREA: Ciências Exatas e da Terra
ÁREA: Ciência da Computação
SUBÁREA: Metodologia e Técnicas da Computação
ESPECIALIDADE: Sistemas de Informação
RESUMO:

Reinforcement learning (RL) aims to build intelligent agents able to optimally act after training process in order to solve a given goal task in an autonomous and non-deterministic fashion. It has been successfully employed in several areas, however, few RL-based approaches related to genome assembly have been found, especially when considering real input datasets. De novo genome assembly is a crucial step in a number of genome projects, but due to its high complexity, the outcome of state-of-art assemblers is still insufficient to properly assist researchers in answering all their scientific questions. Hence, the development of better assembler is desirable and perhaps necessary, and preliminary studies suggest that RL has the potential to solve this computational task. In this sense, this paper presents an empirical analysis to evaluate this hypothesis, particularly in higher scale, through performance assessment along with time and space complexity analysis of a theoretical approach to the problem of assembly proposed by [2] using the RL algorithm Q-learning. Our analysis shows that, although space and time complexities are limiting scale issues, RL is shown as a powerful, alternative possibility for solving the DNA fragment assembly problem.

MEMBROS DA BANCA:
Presidente - 381.226.502-87 - RONNIE CLEY DE OLIVEIRA ALVES - UFRGS
Interno - 2323064 - FILIPE DE OLIVEIRA SARAIVA
Externo ao Programa - 2324982 - REGIANE SILVA KAWASAKI FRANCES