Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: A robust approach based on Weibull distribution for clustering gene expression data. | Wang et al. Algorithms for Molecular Biology 2011 6 14 http content 6 1 14 AMR ALGORITHMS FOR MOLECULAR BIOLOGY RESEARCH Open Access A robust approach based on Weibull distribution for clustering gene expression data Huakun Wang1 2t Zhenzhen Wang1t Xia Li 1 Binsheng Gong1 Lixin Feng2 and Ying Zhou2 Abstract Background Clustering is a widely used technique for analysis of gene expression data. Most clustering methods group genes based on the distances while few methods group genes according to the similarities of the distributions of the gene expression levels. Furthermore as the biological annotation resources accumulated an increasing number of genes have been annotated into functional categories. As a result evaluating the performance of clustering methods in terms of the functional consistency of the resulting clusters is of great interest. Results In this paper we proposed the WDCM Weibull Distribution-based Clustering Method a robust approach for clustering gene expression data in which the gene expressions of individual genes are considered as the random variables following unique Weibull distributions. Our WDCM is based on the concept that the genes with similar expression profiles have similar distribution parameters and thus the genes are clustered via the Weibull distribution parameters. We used the WDCM to cluster three cancer gene expression data sets from the lung cancer B-cell follicular lymphoma and bladder carcinoma and obtained well-clustered results. We compared the performance of WDCM with k-means and Self Organizing Map SOM using functional annotation information given by the Gene Ontology GO . The results showed that the functional annotation ratios of WDCM are higher than those of the other methods. We also utilized the external measure Adjusted Rand Index to validate the performance of the WDCM. The comparative results demonstrate that the WDCM provides the better clustering performance compared to k-means and SOM algorithms. .
