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T. I. Lee, N. J. Rinaldi, F. Robert, D. T. Odom, Z. Bar-Joseph, G. K. Gerber, N. M. Hannett, C. T. Harbison, C. M. Thompson, I. Simon, et al., (2002) Tanscriptional regulatory networks in Saccharomyces cerevisiae, Science, 298, 799–804.

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• TITLE: Incorporating heterogeneous biological data sources in clustering gene expression data

  AUTHORS: Gang-Guo Li, Zheng-Zhi Wang

  KEYWORDS: Statistical Analysis; Similarity/ Dissimilarity Measure; Gene Expression Data; Clustering; Data Fusion

  JOURNAL NAME: Health, Vol.1 No.1, June 3, 2009

  ABSTRACT: In this paper, a similarity measure between genes with protein-protein interactions is pro-posed. The chip-chip data are converted into the same form of gene expression data with pear-son correlation as its similarity measure. On the basis of the similarity measures of protein- protein interaction data and chip-chip data, the combined dissimilarity measure is defined. The combined distance measure is introduced into K-means method, which can be considered as an improved K-means method. The improved K-means method and other three clustering methods are evaluated by a real dataset. Per-formance of these methods is assessed by a prediction accuracy analysis through known gene annotations. Our results show that the improved K-means method outperforms other clustering methods. The performance of the improved K-means method is also tested by varying the tuning coefficients of the combined dissimilarity measure. The results show that it is very helpful and meaningful to incorporate het-erogeneous data sources in clustering gene expression data, and those coefficients for the genome-wide or completed data sources should be given larger values when constructing the combined dissimilarity measure.