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• W2001835162 abstract "Existing methods for analyzing bacterial CGH data from two-color arrays are based on log-ratios only, a paradigm inherited from expression studies. We propose an alternative approach, where microarray signals are used in a different way and sequence identity is predicted using a supervised learning approach. A data set containing 32 hybridizations of sequenced versus sequenced genomes have been used to test and compare methods. A ROC-analysis has been performed to illustrate the ability to rank probes with respect to Present/Absent calls. Classification into Present and Absent is compared with that of a gaussian mixture model. The results indicate our proposed method is an improvement of existing methods with respect to ranking and classification of probes, especially for multi-genome arrays." @default.
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• W2001835162 date "2009-03-19" @default.
• W2001835162 modified "2023-10-10" @default.
• W2001835162 title "Improved analysis of bacterial CGH data beyond the log-ratio paradigm" @default.
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• W2001835162 doi "https://doi.org/10.1186/1471-2105-10-91" @default.
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