loading
Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Adara Nogueira 1 ; Artur Ferreira 2 ; 1 and Mário Figueiredo 2 ; 3

Affiliations: 1 ISEL, Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Portugal ; 2 Instituto de Telecomunicações, Lisboa, Portugal ; 3 IST, Instituto Superior Técnico, Universidade de Lisboa, Portugal

Keyword(s): Machine Learning, Feature Selection, Feature Discretization, Microarray Data, Cancer Explainability.

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 pro viding valuable information about which genes are involved in a given disease. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.204.56.185

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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 - ICPRAM, ISBN 978-989-758-549-4; ISSN 2184-4313, pages 362-369. DOI: 10.5220/0010980100003122

@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 - ICPRAM,},
year={2022},
pages={362-369},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010980100003122},
isbn={978-989-758-549-4},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM,
TI - A Step Towards the Explainability of Microarray Data for Cancer Diagnosis with Machine Learning Techniques
SN - 978-989-758-549-4
IS - 2184-4313
AU - Nogueira, A.
AU - Ferreira, A.
AU - Figueiredo, M.
PY - 2022
SP - 362
EP - 369
DO - 10.5220/0010980100003122