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

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

Research output: Chapter in Book/Report › Chapter (peer-reviewed) › peer-review

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.

Translated title of the contribution	Comparación de modelos de aprendizaje automático para la predicción de células cancerosas mediante complejos MHC de clase I
Original language	English (US)
Title of host publication	16th International Symposium on Medical Information Processing and Analysis
Editors	Eduardo Romero, Natasha Lepore, Jorge Brieva, Marius Linguraru
Publisher	SPIE
Volume	11583
ISBN (Electronic)	9781510639911
DOIs	https://doi.org/10.1117/12.2579602
State	Published - Nov 3 2020
Event	16th International Symposium on Medical Information Processing and Analysis 2020 - Lima, Virtual, Peru Duration: Oct 3 2020 → Oct 4 2020

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	11583
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	16th International Symposium on Medical Information Processing and Analysis 2020
Country/Territory	Peru
City	Lima, Virtual
Period	10/3/20 → 10/4/20

All Science Journal Classification (ASJC) codes

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1117/12.2579602

Cite this

Navas Luquez, M., Orjuela Cañón, A. D., & Charry, O. J. P. (2020). Comparison of machine learning models for the prediction of cancer cells using MHC class I complexes. In E. Romero, N. Lepore, J. Brieva, & M. Linguraru (Eds.), 16th International Symposium on Medical Information Processing and Analysis (Vol. 11583). Article 115830M (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11583). SPIE. https://doi.org/10.1117/12.2579602

Navas Luquez, Mateo ; Orjuela Cañón, Alvaro David ; Charry, Oscar Julián Perdomo. / Comparison of machine learning models for the prediction of cancer cells using MHC class I complexes. 16th International Symposium on Medical Information Processing and Analysis. editor / Eduardo Romero ; Natasha Lepore ; Jorge Brieva ; Marius Linguraru. Vol. 11583 SPIE, 2020. (Proceedings of SPIE - The International Society for Optical Engineering).

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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.",

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Navas Luquez, M, Orjuela Cañón, AD & Charry, OJP 2020, Comparison of machine learning models for the prediction of cancer cells using MHC class I complexes. in E Romero, N Lepore, J Brieva & M Linguraru (eds), 16th International Symposium on Medical Information Processing and Analysis. vol. 11583, 115830M, Proceedings of SPIE - The International Society for Optical Engineering, vol. 11583, SPIE, 16th International Symposium on Medical Information Processing and Analysis 2020, Lima, Virtual, Peru, 10/3/20. https://doi.org/10.1117/12.2579602

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.
16th International Symposium on Medical Information Processing and Analysis. ed. / Eduardo Romero; Natasha Lepore; Jorge Brieva; Marius Linguraru. Vol. 11583 SPIE, 2020. 115830M (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11583).

Research output: Chapter in Book/Report › Chapter (peer-reviewed) › peer-review

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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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Navas Luquez M, Orjuela Cañón AD, Charry OJP. Comparison of machine learning models for the prediction of cancer cells using MHC class I complexes. In Romero E, Lepore N, Brieva J, Linguraru M, editors, 16th International Symposium on Medical Information Processing and Analysis. Vol. 11583. SPIE. 2020. 115830M. (Proceedings of SPIE - The International Society for Optical Engineering). doi: 10.1117/12.2579602

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

Abstract

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All Science Journal Classification (ASJC) codes

UN SDGs

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