loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

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

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

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 clusteri ng 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)

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 3.144.102.239

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:
Šašić, I.; Brdar, S.; Lončar-Turukalo, T.; Aidos, H. and Fred, A. (2017). Consensus Clustering for Cancer Gene Expression Data. In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - BIOINFORMATICS; ISBN 978-989-758-214-1; ISSN 2184-4305, SciTePress, pages 176-183. DOI: 10.5220/0006174501760183

@conference{bioinformatics17,
author={Isidora Šašić. and Sanja Brdar. and Tatjana Lončar{-}Turukalo. and Helena Aidos. and Ana Fred.},
title={Consensus Clustering for Cancer Gene Expression Data},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - BIOINFORMATICS},
year={2017},
pages={176-183},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006174501760183},
isbn={978-989-758-214-1},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - BIOINFORMATICS
TI - Consensus Clustering for Cancer Gene Expression Data
SN - 978-989-758-214-1
IS - 2184-4305
AU - Šašić, I.
AU - Brdar, S.
AU - Lončar-Turukalo, T.
AU - Aidos, H.
AU - Fred, A.
PY - 2017
SP - 176
EP - 183
DO - 10.5220/0006174501760183
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic