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

Mateo N.; Álvaro David Orjuela Canon; Oscar Julían Perdomo Charry

doi:10.1117/12.2579602

3 November 2020 Comparison of machine learning models for the prediction of cancer cells using MHC class I complexes

Mateo N., Álvaro David Orjuela Canon, Oscar Julían Perdomo Charry

Proceedings Volume 11583, 16th International Symposium on Medical Information Processing and Analysis; 115830M (2020) https://doi.org/10.1117/12.2579602
Event: The 16th International Symposium on Medical Information Processing and Analysis, 2020, Lima, Peru

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.

Citation Download Citation

Mateo N., Álvaro David Orjuela Canon, and Oscar Julían Perdomo Charry "Comparison of machine learning models for the prediction of cancer cells using MHC class I complexes", Proc. SPIE 11583, 16th International Symposium on Medical Information Processing and Analysis, 115830M (3 November 2020); https://doi.org/10.1117/12.2579602

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available