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
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Comparison of machine learning models for the prediction of cancer cells using MHC class I complexes |
| title |
Comparison of machine learning models for the prediction of cancer cells using MHC class I complexes |
| spellingShingle |
Comparison of machine learning models for the prediction of cancer cells using MHC class I complexes Modelos - Aprendizaje automático Células cancerosas Predictive analytics Análisis predictivo Análisis predictivo |
| title_short |
Comparison of machine learning models for the prediction of cancer cells using MHC class I complexes |
| title_full |
Comparison of machine learning models for the prediction of cancer cells using MHC class I complexes |
| title_fullStr |
Comparison of machine learning models for the prediction of cancer cells using MHC class I complexes |
| title_full_unstemmed |
Comparison of machine learning models for the prediction of cancer cells using MHC class I complexes |
| title_sort |
Comparison of machine learning models for the prediction of cancer cells using MHC class I complexes |
| dc.creator.fl_str_mv |
Orjuela Canon, Alvaro David Perdomo Charry, Oscar Julian |
| dc.contributor.author.none.fl_str_mv |
Orjuela Canon, Alvaro David Perdomo Charry, Oscar Julian |
| dc.contributor.researchgroup.spa.fl_str_mv |
GiBiome |
| dc.subject.armarc.none.fl_str_mv |
Modelos - Aprendizaje automático Células cancerosas |
| topic |
Modelos - Aprendizaje automático Células cancerosas Predictive analytics Análisis predictivo Análisis predictivo |
| dc.subject.armarc.eng.fl_str_mv |
Predictive analytics |
| dc.subject.armarc.spa.fl_str_mv |
Análisis predictivo Análisis predictivo |
| description |
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. |
| publishDate |
2020 |
| dc.date.issued.none.fl_str_mv |
2020 |
| dc.date.accessioned.none.fl_str_mv |
2021-05-24T17:48:41Z 2021-10-01T17:16:51Z |
| dc.date.available.none.fl_str_mv |
2021-05-24 2021-10-01T17:16:51Z |
| dc.type.spa.fl_str_mv |
Artículo de revista |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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0277-786X |
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https://repositorio.escuelaing.edu.co/handle/001/1471 |
| dc.identifier.doi.none.fl_str_mv |
10.1117/12.2579602 |
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https://doi.org/10.1117/12.2579602 |
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0277-786X 10.1117/12.2579602 |
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https://repositorio.escuelaing.edu.co/handle/001/1471 https://doi.org/10.1117/12.2579602 |
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eng |
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eng |
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8 |
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1 |
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11583 |
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N/A |
| dc.relation.ispartofjournal.spa.fl_str_mv |
Proceedings Of Spie, The International Society For Optical Engineering |
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http://purl.org/coar/access_right/c_14cb |
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closedAccess |
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application/pdf |
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SPIE |
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Estados Unidos |
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https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11583/2579602/Comparison-of-machine-learning-models-for-the-prediction-of-cancer/10.1117/12.2579602.short?SSO=1&tab=ArticleLinkCited |
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Escuela Colombiana de Ingeniería Julio Garavito |
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Orjuela Canon, Alvaro Davidcc8f86677d6dc983cbfe50be82b9e516600Perdomo Charry, Oscar Julianc280ba13fd48e8dbf9cdbc8179aa9c94600GiBiome2021-05-24T17:48:41Z2021-10-01T17:16:51Z2021-05-242021-10-01T17:16:51Z20200277-786Xhttps://repositorio.escuelaing.edu.co/handle/001/147110.1117/12.2579602https://doi.org/10.1117/12.2579602Currently, 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.En la actualidad, el cáncer es la principal causa de muerte en todo el mundo y provoca millones de fallecimientos anuales en los países en desarrollo debido a la escasez de detección y tratamiento. La detección temprana de los neoantígenos del cáncer es útil para los especialistas, ya que pueden ayudar en el desarrollo de tratamientos más exitosos. Partiendo de esta problemática, el objetivo de este trabajo es realizar un proceso comparativo entre modelos de aprendizaje automático, para determinar cuál de ellos permite una adecuada predicción de los datos, y así determinar los neoantígenos cancerígenos. Para ello, se empleó la información extraída de las secuencias de proteínas. Los resultados preliminares muestran una sensibilidad y especificidad de 1,0 y 0,98 respectivamente.application/pdfengSPIEEstados Unidoshttps://www.spiedigitallibrary.org/conference-proceedings-of-spie/11583/2579602/Comparison-of-machine-learning-models-for-the-prediction-of-cancer/10.1117/12.2579602.short?SSO=1&tab=ArticleLinkCitedComparison of machine learning models for the prediction of cancer cells using MHC class I complexesArtículo de revistainfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a858111583N/AProceedings Of Spie, The International Society For Optical Engineeringinfo:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbModelos - Aprendizaje automáticoCélulas cancerosasPredictive analyticsAnálisis predictivoAnálisis predictivoORIGINALComparison of machine learning models for the prediction of cancer 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