Anomalies Detection in Gene Expression Matrices: Towards a New Approach

Nicoletta Del Buono, Flavia Esposito, Flavia Esposito, Laura Selicato, Maria Carmela Vegliante

2021

Abstract

One of the main problems in analyzing real data is often related to the presence of anomalies. Anomalous cases may, in fact, spoil the resulting analysis as well as contain valuable information at the same time. In both cases, the ability to detect these occurrences is very important. Particularly, in biomedical field, a proper identification of outliers allows to develop novel biological hypotheses not taken into consideration when experimental biological data are considered. In this paper, we address the problem of detecting outlier samples in gene expression data. We propose an ensemble approach for anomalies detection in gene expression matrices based on the use of hierarchical clustering and Robust Principal Component Analysis, that allows to derive a novel pseudo mathematical classification of anomalies.

Paper Citation

in Harvard Style

Buono N., Esposito F., Selicato L. and Vegliante M. (2021). Anomalies Detection in Gene Expression Matrices: Towards a New Approach. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 3: BIOINFORMATICS; ISBN 978-989-758-490-9, SciTePress, pages 162-169. DOI: 10.5220/0010342300002865

in Bibtex Style

@conference{bioinformatics21,
author={Nicoletta Del Buono and Flavia Esposito and Laura Selicato and Maria Carmela Vegliante},
title={Anomalies Detection in Gene Expression Matrices: Towards a New Approach},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 3: BIOINFORMATICS},
year={2021},
pages={162-169},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010342300002865},
isbn={978-989-758-490-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 3: BIOINFORMATICS
TI - Anomalies Detection in Gene Expression Matrices: Towards a New Approach
SN - 978-989-758-490-9
AU - Buono N.
AU - Esposito F.
AU - Selicato L.
AU - Vegliante M.
PY - 2021
SP - 162
EP - 169
DO - 10.5220/0010342300002865
PB - SciTePress