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

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  • Hu Yang, Cent Univ Finance & Econ, Central University of Finance & Economics, Sch Informat
  • ,
  • Xiaoqin Liu

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.

Original languageEnglish
JournalJournal of Computational Biology
Pages (from-to)689-698
Number of pages10
Publication statusPublished - Jul 2017

    Research areas

  • bidirectional penalty, clustering, gene expression data, high-dimensional data, penalization, HIGH-DIMENSIONAL DATA, VARIABLE SELECTION, LASSO, CLASSIFICATION, REGRESSION, FRAMEWORK, NUMBER, MODEL

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