Titlebook: Methods of Microarray Data Analysis II; Papers from CAMDA ’0 Simon M. Lin,Kimberly F. Johnson Book 2002 Springer Science+Business Media New

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巧思

天賦

Biology-driven Clustering of Microarray Data,e picture of the heterogeneity of different kinds of cancer. We also get information about the importance of different chromosomes and biological processes for distinguishing cancers. In general, methods that use existing biological knowledge are likely to provide more meaningful and more interpretable results than completely unsupervised methods.

GULLY

MONY

Analysis of Gene Expression Profiles and Drug Activity Patterns by Clustering and Bayesian Network some biologically meaningful relationships among gene expression levels, drug activities, and cancer types, suggesting the usefulness of Bayesian network learning as a method for exploratory analysis of high-throughput genomic data.

過份艷麗

Extracting Knowledge from Genomic Experiments by Incorporating the Biomedical Literature,nd analyzed a set of 81 terms that can be grouped together via the literature. In addition, there is literature directly linking 52 of the terms with AML. Overall, the literature analysis identified 1028 sentences that directly relate two or more of the query genes.

antiandrogen

Book 2002nd beyond. The recent explosion of this technology threatens to overwhelm the scientific community with massive quantities of data. Because microarray data analysis is an emerging field, very few analytical models currently exist. .Methods of Microarray Data Analysis II. is the second book in this p

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