Evaluación de métodos de aprendizaje automático aplicados al proceso de detección de bacteria-bacteriófago

The misuse of antibiotic drugs contributes to the emergence and rapid dissemination of antibiotic resistance worldwide, threatening medical progress. The development of innovative alternatives is necessary to fight against this public health problem. A re-emerging therapy, dubbed phage-therapy, might represent such an alternative. Phage-therapy is based on viruses (bacteriophages) that specifically infect and kill bacteria during their life cycle. The success of phage therapy mainly relies on the exact matching between the pathogenic bacteria and the therapeutic phage. However, this is a time-consuming process achieved in laboratories and time is a precious and critical resource in a clinical context. Hence, the fast identification of potential phage candidates capable of dealing with a given bacteria is essential for using phage-therapy in routine. Machine learning algorithms trained on public genome databases constitute a promising approach to achieve this goal. Unfortunately, public databases contain highly imbalanced interaction data (i.e., mostly positive phage-bacterium interactions); making it harder to use classic machine learning algorithms that needs relatively-balanced classes to work. To address this problem, we are exploring the use of One-Class learning methods, which are robust tools to deal with imbalanced datasets. We have tested an odd number of One-Class learning techniques merged with the ensemble-learning paradigm on real medical data presenting accuracy results from 75% up to 85%, encouraging towards tailoring and scaling them up to our phagebacteria data. Using gridsearch and pareto fronts to refine the algorithms, the results on the phage-bacteria interactions datasets were promising showing good performance and scalability. Further work could include developing new methods for One-Class classification and applying them to other type of real data as well as try the algorithms with real tested negative interactions

• Autores: López Silva, Juan Fernando
• Tipo de recurso: Trabajo de grado de pregrado
• Fecha de publicación: 2019
• Institución: Universidad Autónoma de Occidente
• Repositorio: RED: Repositorio Educativo Digital UAO
• Idioma: eng
• OAI Identifier: oai:red.uao.edu.co:10614/10756
• Acceso en línea: http://hdl.handle.net/10614/10756
• Palabra clave: Ingeniería Mecatrónica; Aprendizaje automático (Inteligencia artificial); Resistencia a los medicamentos en microorganismos; Bacteriófagos; Algoritmos
• Rights: openAccess
• License: Derechos Reservados - Universidad Autónoma de Occidente