Motor de caracterización de enzimas capaces de degradar compuestos aromáticos perteneciente a la familia de los hidrocarburos
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Date
2023-05
Journal Title
Journal ISSN
Volume Title
Program
Ingeniería Civil Telemática
Campus
Campus Casa Central Valparaíso
Abstract
En este documento se propone una solución para la caracterización de microorganismos capaces de degradar compuestos aromáticos pertenecientes a la familia de los hidrocarburos. El objetivo es identificar patrones dentro de las secuencias aminoacídicas que permitan detectar posibles funciones enzimáticas, lo que ayudaría a entender y caracterizar los microorganismos para su uso en la industria o en la investigación. La propuesta se basa en una arquitectura de software que utiliza machine learning para realizar la caracterización. Además, se describe detalladamente cómo implementar esta arquitectura, incluyendo el proceso de recopilación y preparación de datos, el entrenamiento del modelo de machine learning y la evaluación de su exactitud. Al final del proceso, se analizan los resultados obtenidos y se discuten las posibles mejoras y limitaciones de la arquitectura implementada.
This document proposes a solution for the characterization of microorganisms capable of degrading aromatic compounds belonging to the hydrocarbon family. The objective is to identify patterns within amino acid sequences that allow the detection of possible enzymatic functions, which would help understand and characterize microorganisms for use in industry or research. The proposal is based on a software architecture that uses machine learning to perform the characterization. In addition, it describes in detail how to implement this architecture, including the process of data collection and preparation, training the machine learning model, and evaluating its accuracy. At the end of the process, the results obtained are analyzed, and the possible improvements and limitations of the implemented architecture are discussed.
This document proposes a solution for the characterization of microorganisms capable of degrading aromatic compounds belonging to the hydrocarbon family. The objective is to identify patterns within amino acid sequences that allow the detection of possible enzymatic functions, which would help understand and characterize microorganisms for use in industry or research. The proposal is based on a software architecture that uses machine learning to perform the characterization. In addition, it describes in detail how to implement this architecture, including the process of data collection and preparation, training the machine learning model, and evaluating its accuracy. At the end of the process, the results obtained are analyzed, and the possible improvements and limitations of the implemented architecture are discussed.
Description
Keywords
Arquitectura de software, Procesamiento de datos, Reciclaje de nutrientes