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The most commonly used method is the cause-specific hazard method, which is utilized in many microarray literatures [36].
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This contrasts with many microarray studies in the literature which have poor statistical power to detect true positives and thus yield a high proportion of false positives because of multiple testing.
Edges among gene products in such networks [for example STRING database (Jensen, 2009)] are scored based on integration of different sources of information such as high-throughput experiments, physical binding extracted from literature and coexpression networks built from many microarray experiments.
This method requires data from many microarray experiments (several hundreds).
RIP-chip violates important assumptions of many microarray analysis methods.
Many complicated prediction rules have been suggested in the microarray literature.
This is a common choice in the microarray literature [ 50].
Many literatures sound contradictory.
Applications of such models are many and literature is growing.
Many ignored the literature.
Are there too many prizes for literature?
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