Comparison of machine learning models for the prediction of cancer cells using MHC class I complexes

Mateo N. Author, Álvaro David Orjuela Canon Author, Oscar Julián Perdomo Charry

Resultado de la investigación: Capítulo en Libro/Reporte/ConferenciaCapítulo (revisado por pares)revisión exhaustiva

Resumen

Currently, cancer is the leading cause of death worldwide, making millions of deaths annually in developing countries due to a shortage of detection and treatment. Early detection of cancer neoantigens is useful for specialists because they can help in the development of more successful treatments. Based on this problem, the objective of this work is to carry out a comparative process between machine learning models, to determine which of them allows an adequate prediction of the data, and thus determine the carcinogenic neoantigens. For this, information extracted from protein sequences was employed. The preliminary results show sensitivity and specificity of 1.0 and 0.98 respectively.

Idioma originalInglés estadounidense
Título de la publicación alojada16th International Symposium on Medical Information Processing and Analysis
EditoresEduardo Romero, Natasha Lepore, Jorge Brieva, Marius Linguraru
EditorialSPIE
ISBN (versión digital)9781510639911
DOI
EstadoPublicada - nov 3 2020
Evento16th International Symposium on Medical Information Processing and Analysis 2020 - Lima, Virtual, Perú
Duración: oct 3 2020oct 4 2020

Serie de la publicación

NombreProceedings of SPIE - The International Society for Optical Engineering
Volumen11583
ISSN (versión impresa)0277-786X
ISSN (versión digital)1996-756X

Conferencia

Conferencia16th International Symposium on Medical Information Processing and Analysis 2020
País/TerritorioPerú
CiudadLima, Virtual
Período10/3/2010/4/20

All Science Journal Classification (ASJC) codes

  • Materiales electrónicos, ópticos y magnéticos
  • Física de la materia condensada
  • Informática aplicada
  • Matemáticas aplicadas
  • Ingeniería eléctrica y electrónica

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