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

Mateo Navas Luquez; Alvaro David Orjuela Cañón; Oscar Julián Perdomo Charry

doi:10.1117/12.2579602

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

Título traducido de la contribución: Comparación de modelos de aprendizaje automático para la predicción de células cancerosas mediante complejos MHC de clase I

Mateo Navas Luquez
, Alvaro David Orjuela Cañón
, Oscar Julián Perdomo Charry

Producción científica: Redes de conocimiento › Actas de congreso › revisión exhaustiva

Resumen

En la actualidad, el cáncer es la principal causa de muerte en todo el mundo, con millones de fallecimientos anuales en los países en desarrollo debido a la escasez de detección y tratamiento. La detección precoz 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 información extraída de secuencias de proteínas. Los resultados preliminares muestran una sensibilidad y especificidad de 1,0 y 0,98 respectivamente.

Título traducido de la contribución	Comparación de modelos de aprendizaje automático para la predicción de células cancerosas mediante complejos MHC de clase I
Idioma original	Inglés estadounidense
DOI	https://doi.org/10.1117/12.2579602
Estado	Publicada - nov. 3 2020
Evento	16th International Symposium on Medical Information Processing and Analysis 2020 - Lima, Virtual, Perú Duración: oct. 3 2020 → oct. 4 2020

Conferencia

Conferencia	16th International Symposium on Medical Information Processing and Analysis 2020
País/Territorio	Perú
Ciudad	Lima, Virtual
Período	10/3/20 → 10/4/20

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 3: Salud y bienestar

Áreas temáticas de ASJC Scopus

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

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title = "Comparison of machine learning models for the prediction of cancer cells using MHC class I complexes",

abstract = "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.",

author = "\{Navas Luquez\}, Mateo and \{Orjuela Ca{\~n}{\'o}n\}, \{Alvaro David\} and Charry, \{Oscar Juli{\'a}n Perdomo\}",

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Comparison of machine learning models for the prediction of cancer cells using MHC class I complexes. / Navas Luquez, Mateo; Orjuela Cañón, Alvaro David; Charry, Oscar Julián Perdomo.
2020. Papel presentado en 16th International Symposium on Medical Information Processing and Analysis 2020, Lima, Virtual, Perú.

Producción científica: Redes de conocimiento › Actas de congreso › revisión exhaustiva

TY - CONF

T1 - Comparison of machine learning models for the prediction of cancer cells using MHC class I complexes

AU - Navas Luquez, Mateo

AU - Orjuela Cañón, Alvaro David

AU - Charry, Oscar Julián Perdomo

PY - 2020/11/3

Y1 - 2020/11/3

N2 - 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.

AB - 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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T2 - 16th International Symposium on Medical Information Processing and Analysis 2020

Y2 - 3 October 2020 through 4 October 2020

ER -

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

Resumen

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