Comparison of machine learning models for the prediction of cancer cells using MHC class I complexes
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....
- Autores:
-
Orjuela Canon, Alvaro David
Perdomo Charry, Oscar Julian
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2020
- Institución:
- Escuela Colombiana de Ingeniería Julio Garavito
- Repositorio:
- Repositorio Institucional ECI
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.escuelaing.edu.co:001/1471
- Acceso en línea:
- https://repositorio.escuelaing.edu.co/handle/001/1471
https://doi.org/10.1117/12.2579602
- Palabra clave:
- Modelos - Aprendizaje automático
Células cancerosas
Predictive analytics
Análisis predictivo
Análisis predictivo
- Rights
- closedAccess
- License
- http://purl.org/coar/access_right/c_14cb
| Summary: | 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. |
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