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Authors: Isidora Šašić 1 ; Sanja Brdar 2 ; Tatjana Lončar-Turukalo 1 ; Helena Aidos 3 and Ana Fred 3

Affiliations: 1 University of Novi Sad, Serbia ; 2 BioSense Institute, Serbia ; 3 Instituto Superior Tecnico, Portugal

ISBN: 978-989-758-214-1

Keyword(s): Clustering, Consensus Clustering, Cancer Gene Expression.

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

Abstract: Clustering algorithms are extensively used on patient tissue samples in order to group and visualize the microarray data. The high dimensionality and probe specific noise make the selection of the appropriate clustering algorithm an uneasy task. This study presents a large-scale analysis of three clustering algorithms: k-means, hierarchical clustering (HC) and evidence accumulation clustering (EAC) on thirty-five cancer gene expression data sets selected to benchmark the performance of the clustering algorithms. Separated performance analysis was done on data sets from Affymetrix and cDNA chip platforms to examine the possible influence of the microarray technology. The study revealed no consistent algorithm ranking can be inferred, though in general EAC presented the best compromise of adjusted rand index (ARI) and variance. However, the results indicated that ARI variance under repeated k-means initializations offers useful information on the need to implement more complex clusterin g techniques. If repeated K-means converges to the same partition, also confirmed by the HC clustering, there is no need to run EAC. However, under moderate or highly variable ARI in repeated K-means, EAC should be used to reduce the uncertainty of clustering and unveil the data structure. (More)

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Paper citation in several formats:
Šašić, I.; Brdar, S.; Lončar-Turukalo, T.; Lončar-Turukalo, T.; Aidos, H. and Fred, A. (2017). Consensus Clustering for Cancer Gene Expression Data - Large-Scale Analysis using Evidence Accumulation Approach.In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017) ISBN 978-989-758-214-1, pages 176-183. DOI: 10.5220/0006174501760183

@conference{bioinformatics17,
author={Isidora Šašić. and Sanja Brdar. and Tatjana Lončar{-}Turukalo. and Tatjana Lončar{-}Turukalo. and Helena Aidos. and Ana Fred.},
title={Consensus Clustering for Cancer Gene Expression Data - Large-Scale Analysis using Evidence Accumulation Approach},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017)},
year={2017},
pages={176-183},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006174501760183},
isbn={978-989-758-214-1},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017)
TI - Consensus Clustering for Cancer Gene Expression Data - Large-Scale Analysis using Evidence Accumulation Approach
SN - 978-989-758-214-1
AU - Šašić, I.
AU - Brdar, S.
AU - Lončar-Turukalo, T.
AU - Lončar-Turukalo, T.
AU - Aidos, H.
AU - Fred, A.
PY - 2017
SP - 176
EP - 183
DO - 10.5220/0006174501760183

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