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/ReportChapter (peer-reviewed)peer-review


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 contributionComparación de modelos de aprendizaje automático para la predicción de células cancerosas mediante complejos MHC de clase I
Original languageEnglish (US)
Title of host publication16th International Symposium on Medical Information Processing and Analysis
EditorsEduardo Romero, Natasha Lepore, Jorge Brieva, Marius Linguraru
ISBN (Electronic)9781510639911
StatePublished - Nov 3 2020
Event16th International Symposium on Medical Information Processing and Analysis 2020 - Lima, Virtual, Peru
Duration: Oct 3 2020Oct 4 2020

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


Conference16th International Symposium on Medical Information Processing and Analysis 2020
CityLima, Virtual

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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