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Authors: Fernanda Brito Correia 1 ; Joel P. Arrais 2 and José Luis Oliveira 3

Affiliations: 1 University of Aveiro and Polytechnic Institute of Coimbra, Portugal ; 2 University of Coimbra, Portugal ; 3 University of Aveiro, Portugal

ISBN: 978-989-758-170-0

Keyword(s): Protein-protein Interaction Networks, Classification, Cancer Prediction.

Related Ontology Subjects/Areas/Topics: Bioinformatics ; Biomedical Engineering ; Data Mining and Machine Learning ; Pattern Recognition, Clustering and Classification ; Systems Biology

Abstract: Several data mining methods have been applied to explore biological data and understand the mechanisms that regulate genetic and metabolic diseases. The underlying hypothesis is that the identification of signatures can help the clinical identification of diseased tissues. Under this principle many different methodologies have been tested mostly using unsupervised methods. A common trend consists in combining the information obtained from gene expression and protein-protein interaction networks analyses or, more recently, building series of complex networks to model system dynamics. Despite the positive results that these works present, they typically fail to generalize out of sample datasets. In this paper we describe a supervised classification approach, with a new methodology for extracting the network topology dynamics embedded in a disease system, to improve the capacity of cancer prediction, using exclusively the topological properties of biological networks as features. Four mi croarrays datasets were used, for testing and validation, three from breast cancer experiments and one from a liver cancer experiment. The obtained results corroborate the potential of the proposed methodology to predict a certain type of cancer and the necessity of applying different classification models to different types of cancer. (More)

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Paper citation in several formats:
Correia, F.; Arrais, J. and Oliveira, J. (2016). Prediction of Cancer using Network Topological Features.In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 207-215. DOI: 10.5220/0005696202070215

@conference{bioinformatics16,
author={Fernanda Brito Correia. and Joel P. Arrais. and José Luis Oliveira.},
title={Prediction of Cancer using Network Topological Features},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2016)},
year={2016},
pages={207-215},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005696202070215},
isbn={978-989-758-170-0},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2016)
TI - Prediction of Cancer using Network Topological Features
SN - 978-989-758-170-0
AU - Correia, F.
AU - Arrais, J.
AU - Oliveira, J.
PY - 2016
SP - 207
EP - 215
DO - 10.5220/0005696202070215

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