Aprendizaje de máquina y aprendizaje profundo en biotecnología: aplicaciones, impactos y desafíos
| Issue | Vol. 2 Núm. 2 (2019): Ciencia, Ambiente y Clima |
| DOI | |
| Publicado | dic 14, 2019 |
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Estadísticas |
Resumen
La bioinformática es un área que ha modificado la forma en que se diseñan y se desarrollan los experimentos e investigaciones de las áreas biológicas. La biotecnología no ha quedado fuera de los alcances de la bioinformática, impactando directamente áreas como el descubrimiento y el desarrollo de fármacos, mejoramiento de cultivos, biorremediación, estudios de la diversidad ambiental, patología molecular, entre otras. Esto se debe, en gran medida, al desarrollo de las tecnologías de secuenciación de alto rendimiento o Next-generation sequencing (NGS), que han generado gran cantidad de datos que deben ser procesados y analizados para producir nuevos conocimientos y descubrimientos. Lo anterior ha promovido que dos áreas de la bioinformática y la ciencia de la computación, machine learning y deep learning, hayan sido utilizadas para el análisis de estos datos. El “aprendizaje de máquina” aplica técnicas que permiten que las computadoras aprendan, mientras que el “aprendizaje profundo” genera modelos de redes neuronales artificiales que intenta imitar el funcionamiento del cerebro humano, permitiéndoles aprender a partir de los datos y mejorar su aprendizaje a través de las experiencias. Estas dos áreas son esenciales para poder identificar, analizar, interpretar y obtener conocimientos de la gran cantidad de datos biológicos (Big biological data). En este trabajo hacemos una revisión de estas dos áreas: el aprendizaje de máquina y el aprendizaje profundo, orientado al impacto y sus aplicaciones en el área de biotecnología.
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aprendizaje de máquina
aprendizaje pro-fundo
biotecnología
bioinformática
datos biológicos
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© Ciencia, Ambiente y Clima, 2019
Afiliaciones
Edian F. Franco
Universidad Federal de Para, Belém, Pará, Brasil.
Rommel J. Ramos
Universidad Federal de Para, Belém, Pará, Brasil.
Cómo citar
F. Franco, E., & J. Ramos, R. (2019). Aprendizaje de máquina y aprendizaje profundo en biotecnología: aplicaciones, impactos y desafíos. Ciencia, Ambiente Y Clima, 2(2), 7–26. https://doi.org/10.22206/cac.2019.v2i2.pp7-26