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Both features are critically important for increasing the power of identifying co-expressed genes in large scale gene expression datasets.
When dealing with large scale gene expression studies, observations are commonly contaminated by unwanted variation factors such as platforms or batches.
These algorithms will greatly facilitate the elucidation of regulatory networks using large scale gene expression profile data.
Large scale gene expression analysis has become an essential tool for many biological and medical investigations.
This integrated genome annotation will reduce database variation bias in large scale gene expression studies.
For many years microarrays remained the primary source of knowledge pertaining to large scale gene expression.
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Large-scale gene expression studies have mainly focused on highly expressed and 'discriminatory' genes to decipher key regulatory processes.
In large-scale gene expression data, a module is usually defined as a group of co-expressed genes.
We demonstrate how the outputs created from sigQC can be used for the evaluation of gene signatures on large-scale gene expression datasets.
The evolvement of large-scale gene expression analysis methodologies provided a promising tool to understand the cellular events underlying the mood-stabilizing effect of the drug.
Sun N, Ma L, Pan D, Zhao H, Deng XW. (2003) Evaluation of light regulatory potential of Calvin cycle steps based on large-scale gene expression profiling data.
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