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: Knowledge networks › Conference proceedings › 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)
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

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

UN SDGs

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

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

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

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10.1117/12.2579602

Cite this

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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\}",

note = "Publisher Copyright: {\textcopyright} 2020 SPIE Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 16th International Symposium on Medical Information Processing and Analysis 2020 ; Conference date: 03-10-2020 Through 04-10-2020",

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doi = "10.1117/12.2579602",

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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. Paper presented at 16th International Symposium on Medical Information Processing and Analysis 2020, Lima, Virtual, Peru.

Research output: Knowledge networks › Conference proceedings › peer-review

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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UR - https://www.scopus.com/pages/publications/85096819703#tab=citedBy

U2 - 10.1117/12.2579602

DO - 10.1117/12.2579602

M3 - Conference proceedings

AN - SCOPUS:85096819703

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

Abstract

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

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