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  • Cluster Analysis of Gene Expression Data Based on Self-Splitting and Merging Competitive Learning

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    Author(s)
    Wu, SH
    Liew, AWC
    Yan, H
    Yang, MS
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Year published
    2004
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    Abstract
    Cluster analysis of gene expression data from a cDNA microarray is useful for identifying biologically relevant groups of genes. However, finding the natural clusters in the data and estimating the correct number of clusters are still two largely unsolved problems. In this paper, we propose a new clustering framework that is able to address both these problems. By using the one-prototype-take-one-cluster (OPTOC) competitive learning paradigm, the proposed algorithm can find natural clusters in the input data, and the clustering solution is not sensitive to initialization. In order to estimate the number of distinct clusters ...
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    Cluster analysis of gene expression data from a cDNA microarray is useful for identifying biologically relevant groups of genes. However, finding the natural clusters in the data and estimating the correct number of clusters are still two largely unsolved problems. In this paper, we propose a new clustering framework that is able to address both these problems. By using the one-prototype-take-one-cluster (OPTOC) competitive learning paradigm, the proposed algorithm can find natural clusters in the input data, and the clustering solution is not sensitive to initialization. In order to estimate the number of distinct clusters in the data, we propose a cluster splitting and merging strategy. We have applied the new algorithm to simulated gene expression data for which the correct distribution of genes over clusters is known a priori. The results show that the proposed algorithm can find natural clusters and give the correct number of clusters. The algorithm has also been tested on real gene expression changes during yeast cell cycle, for which the fundamental patterns of gene expression and assignment of genes to clusters are well understood from numerous previous studies. Comparative studies with several clustering algorithms illustrate the effectiveness of our method.
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    Journal Title
    IEEE Transactions on Information Technology in Biomedicine
    Volume
    8
    Issue
    1
    Publisher URI
    http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4233
    DOI
    https://doi.org/10.1109/TITB.2004.824724
    Copyright Statement
    © 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    Subject
    Information and Computing Sciences
    Engineering
    Medical and Health Sciences
    Publication URI
    http://hdl.handle.net/10072/21805
    Collection
    • Journal articles

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