Automated Machine Learning Strategies to Damage Identification of Neurofibromatosis Mutations

Alvaro David Orjuela-Canon, Juan Carlos Figueroa-Garcia, Roman Neruda

Producción científica: Capítulo en Libro/ReporteContribución a la conferencia

2 Citas (Scopus)

Resumen

Machine learning tools have been employed for problem solutions in bioinformatics. However, the parameters tuning of these models cam imply additional difficulties around the specific technique used to classify. In this work data from protein sequences was applied to three auto machine learning strategies to determine the type of mutation for the Neurofibromatosis disease. Results show that the parameters in the machine learning models were found automatically. In addition, these tools were relevant to determine relations between the amino-acids in the protein sequence.

Idioma originalInglés estadounidense
Título de la publicación alojadaProceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021
EditoresM. Arif Wani, Ishwar K. Sethi, Weisong Shi, Guangzhi Qu, Daniela Stan Raicu, Ruoming Jin
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas1341-1344
Número de páginas4
ISBN (versión digital)9781665443371
DOI
EstadoPublicada - ene. 25 2021
Evento20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021 - Virtual, Online, Estados Unidos
Duración: dic. 13 2021dic. 16 2021

Serie de la publicación

NombreProceedings - 20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021

Conferencia

Conferencia20th IEEE International Conference on Machine Learning and Applications, ICMLA 2021
País/TerritorioEstados Unidos
CiudadVirtual, Online
Período12/13/2112/16/21

Áreas temáticas de ASJC Scopus

  • Seguridad, riesgos, fiabilidad y calidad
  • Informática aplicada a la salud
  • Inteligencia artificial
  • Informática aplicada

Huella

Profundice en los temas de investigación de 'Automated Machine Learning Strategies to Damage Identification of Neurofibromatosis Mutations'. En conjunto forman una huella única.

Citar esto