@inbook{1fcc2e6ffafd490ebaa224bebcc3852a,
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",
year = "2020",
month = nov,
day = "3",
doi = "10.1117/12.2579602",
language = "English (US)",
volume = "11583",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Eduardo Romero and Natasha Lepore and Jorge Brieva and Marius Linguraru",
booktitle = "16th International Symposium on Medical Information Processing and Analysis",
address = "United States",
}