Department of Economics and Business Economics

Studies on the Clustering Algorithm for Analyzing Gene Expression Data with a Bidirectional Penalty

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Studies on the Clustering Algorithm for Analyzing Gene Expression Data with a Bidirectional Penalty. / Yang, Hu; Liu, Xiaoqin.

In: Journal of Computational Biology, Vol. 24, No. 7, 07.2017, p. 689-698.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

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Yang, Hu ; Liu, Xiaoqin. / Studies on the Clustering Algorithm for Analyzing Gene Expression Data with a Bidirectional Penalty. In: Journal of Computational Biology. 2017 ; Vol. 24, No. 7. pp. 689-698.

Bibtex

@article{987a486031d5431b9215760769a22d2b,
title = "Studies on the Clustering Algorithm for Analyzing Gene Expression Data with a Bidirectional Penalty",
abstract = "This article reports a new clustering method based on the k-means algorithm to high-dimensional gene expression data. The proposed approach makes use of bidirectional penalties to constrain the number of clusters and centroids of clusters to simultaneously determine the unknown number of clusters and handle large amounts of noise in gene expression data. Numeric studies indicate that this algorithm not only performs better in clustering but is also comparable to other approaches in its ability to obtain the correct number of clusters and correct signal features. Finally, we apply the proposed approach to analyze two benchmark gene expression datasets. These analyses again indicate that the proposed algorithm performs well in clustering high-dimensional gene expression data with an unknown number of clusters.",
keywords = "bidirectional penalty, clustering, gene expression data, high-dimensional data, penalization, HIGH-DIMENSIONAL DATA, VARIABLE SELECTION, LASSO, CLASSIFICATION, REGRESSION, FRAMEWORK, NUMBER, MODEL",
author = "Hu Yang and Xiaoqin Liu",
year = "2017",
month = jul,
doi = "10.1089/cmb.2017.0051",
language = "English",
volume = "24",
pages = "689--698",
journal = "Journal of Computational Biology",
issn = "1066-5277",
publisher = "Mary Ann Liebert, Inc. publishers",
number = "7",

}

RIS

TY - JOUR

T1 - Studies on the Clustering Algorithm for Analyzing Gene Expression Data with a Bidirectional Penalty

AU - Yang, Hu

AU - Liu, Xiaoqin

PY - 2017/7

Y1 - 2017/7

N2 - This article reports a new clustering method based on the k-means algorithm to high-dimensional gene expression data. The proposed approach makes use of bidirectional penalties to constrain the number of clusters and centroids of clusters to simultaneously determine the unknown number of clusters and handle large amounts of noise in gene expression data. Numeric studies indicate that this algorithm not only performs better in clustering but is also comparable to other approaches in its ability to obtain the correct number of clusters and correct signal features. Finally, we apply the proposed approach to analyze two benchmark gene expression datasets. These analyses again indicate that the proposed algorithm performs well in clustering high-dimensional gene expression data with an unknown number of clusters.

AB - This article reports a new clustering method based on the k-means algorithm to high-dimensional gene expression data. The proposed approach makes use of bidirectional penalties to constrain the number of clusters and centroids of clusters to simultaneously determine the unknown number of clusters and handle large amounts of noise in gene expression data. Numeric studies indicate that this algorithm not only performs better in clustering but is also comparable to other approaches in its ability to obtain the correct number of clusters and correct signal features. Finally, we apply the proposed approach to analyze two benchmark gene expression datasets. These analyses again indicate that the proposed algorithm performs well in clustering high-dimensional gene expression data with an unknown number of clusters.

KW - bidirectional penalty

KW - clustering

KW - gene expression data

KW - high-dimensional data

KW - penalization

KW - HIGH-DIMENSIONAL DATA

KW - VARIABLE SELECTION

KW - LASSO

KW - CLASSIFICATION

KW - REGRESSION

KW - FRAMEWORK

KW - NUMBER

KW - MODEL

U2 - 10.1089/cmb.2017.0051

DO - 10.1089/cmb.2017.0051

M3 - Journal article

C2 - 28489418

VL - 24

SP - 689

EP - 698

JO - Journal of Computational Biology

JF - Journal of Computational Biology

SN - 1066-5277

IS - 7

ER -