A Step Towards the Explainability of Microarray Data for Cancer Diagnosis with Machine Learning Techniques

Adara Nogueira, Artur Ferreira, Artur Ferreira, Mário Figueiredo, Mário Figueiredo

2022

Abstract

Detecting diseases, such as cancer, from from gene expression data has assumed great importance and is a very active area of research. Today, many gene expression datasets are publicly available, which consist of microarray data with information on the activation (or not) of thousands of genes, in sets of patients that have (or not) a certain disease. These datasets consist of high-dimensional feature vectors (very large numbers of genes), which raises difficulties for human analysis and interpretation with the goal of identifying the most relevant genes for detecting the presence of a particular disease. In this paper, we propose to take a step towards the explainability of these disease detection methods, by applying feature discretization and feature selection techniques. We accurately classify microarray data, while substantially reducing and identifying subsets of relevant genes. These small subsets of genes are thus easier to interpret by human experts, thus potentially providing valuable information about which genes are involved in a given disease.

Paper Citation

in Harvard Style

Nogueira A., Ferreira A. and Figueiredo M. (2022). A Step Towards the Explainability of Microarray Data for Cancer Diagnosis with Machine Learning Techniques. In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-549-4, pages 362-369. DOI: 10.5220/0010980100003122

in Bibtex Style

@conference{icpram22,
author={Adara Nogueira and Artur Ferreira and Mário Figueiredo},
title={A Step Towards the Explainability of Microarray Data for Cancer Diagnosis with Machine Learning Techniques},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2022},
pages={362-369},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010980100003122},
isbn={978-989-758-549-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - A Step Towards the Explainability of Microarray Data for Cancer Diagnosis with Machine Learning Techniques
SN - 978-989-758-549-4
AU - Nogueira A.
AU - Ferreira A.
AU - Figueiredo M.
PY - 2022
SP - 362
EP - 369
DO - 10.5220/0010980100003122