Clustering cancer gene expression data by projective clustering ensemble.
Gene expression data analysis has paramount implications for gene treatments, cancer diagnosis and other domains. Clustering is an important and promising tool to analyze gene expression data. Gene expression data is often characterized by a large amount of genes but with limited samples, thus vario...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2017-01-01
|
Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC5325197?pdf=render |
id |
doaj-6d436b3bcb6e482eb0340b418161e2d7 |
---|---|
record_format |
Article |
spelling |
doaj-6d436b3bcb6e482eb0340b418161e2d72020-11-24T21:14:19ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01122e017142910.1371/journal.pone.0171429Clustering cancer gene expression data by projective clustering ensemble.Xianxue YuGuoxian YuJun WangGene expression data analysis has paramount implications for gene treatments, cancer diagnosis and other domains. Clustering is an important and promising tool to analyze gene expression data. Gene expression data is often characterized by a large amount of genes but with limited samples, thus various projective clustering techniques and ensemble techniques have been suggested to combat with these challenges. However, it is rather challenging to synergy these two kinds of techniques together to avoid the curse of dimensionality problem and to boost the performance of gene expression data clustering. In this paper, we employ a projective clustering ensemble (PCE) to integrate the advantages of projective clustering and ensemble clustering, and to avoid the dilemma of combining multiple projective clusterings. Our experimental results on publicly available cancer gene expression data show PCE can improve the quality of clustering gene expression data by at least 4.5% (on average) than other related techniques, including dimensionality reduction based single clustering and ensemble approaches. The empirical study demonstrates that, to further boost the performance of clustering cancer gene expression data, it is necessary and promising to synergy projective clustering with ensemble clustering. PCE can serve as an effective alternative technique for clustering gene expression data.http://europepmc.org/articles/PMC5325197?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xianxue Yu Guoxian Yu Jun Wang |
spellingShingle |
Xianxue Yu Guoxian Yu Jun Wang Clustering cancer gene expression data by projective clustering ensemble. PLoS ONE |
author_facet |
Xianxue Yu Guoxian Yu Jun Wang |
author_sort |
Xianxue Yu |
title |
Clustering cancer gene expression data by projective clustering ensemble. |
title_short |
Clustering cancer gene expression data by projective clustering ensemble. |
title_full |
Clustering cancer gene expression data by projective clustering ensemble. |
title_fullStr |
Clustering cancer gene expression data by projective clustering ensemble. |
title_full_unstemmed |
Clustering cancer gene expression data by projective clustering ensemble. |
title_sort |
clustering cancer gene expression data by projective clustering ensemble. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2017-01-01 |
description |
Gene expression data analysis has paramount implications for gene treatments, cancer diagnosis and other domains. Clustering is an important and promising tool to analyze gene expression data. Gene expression data is often characterized by a large amount of genes but with limited samples, thus various projective clustering techniques and ensemble techniques have been suggested to combat with these challenges. However, it is rather challenging to synergy these two kinds of techniques together to avoid the curse of dimensionality problem and to boost the performance of gene expression data clustering. In this paper, we employ a projective clustering ensemble (PCE) to integrate the advantages of projective clustering and ensemble clustering, and to avoid the dilemma of combining multiple projective clusterings. Our experimental results on publicly available cancer gene expression data show PCE can improve the quality of clustering gene expression data by at least 4.5% (on average) than other related techniques, including dimensionality reduction based single clustering and ensemble approaches. The empirical study demonstrates that, to further boost the performance of clustering cancer gene expression data, it is necessary and promising to synergy projective clustering with ensemble clustering. PCE can serve as an effective alternative technique for clustering gene expression data. |
url |
http://europepmc.org/articles/PMC5325197?pdf=render |
work_keys_str_mv |
AT xianxueyu clusteringcancergeneexpressiondatabyprojectiveclusteringensemble AT guoxianyu clusteringcancergeneexpressiondatabyprojectiveclusteringensemble AT junwang clusteringcancergeneexpressiondatabyprojectiveclusteringensemble |
_version_ |
1716747790582808576 |