Unsupervised Clustering of Bioinformatics Data

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【标题】Unsupervised Clustering of Bioinformatics Data

【作者】 Dimitris K. Tasoulis 

【摘要】The development of microarray technologies gives scientists the ability to examine; discover and monitor the mRNA transcript levels of thousands of genes in a single experiment. Nevertheless; the tremendous amount of data that can be obtained from microarray studies presents a challenge for data analysis. The most commonly used computational approach for analyzing microarray data is cluster analysis. In this paper; we investigate the application of an unsupervised extension of the recently proposed k-windows clustering algorithm on gene expression microarray data. This algorithm apart from identifying the clusters present in a dataset also calculates their number thus no special knowledge about the data is required. To improve the quality of the clustering; we selected the most highly correlated genes with respect to the class distinction of the genes. The results obtained by the application of the algorithm exhibit high classification success.