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While cancer-related mass scale gene expression profiling have almost been exclusive to humans, studies on hormone treatment and gene knock-outs were more common in mice.
We demonstrate that manual collection of mass scale gene expression data will allow derivation of a 'reliability score' for binary expression status of genes.
Nevertheless, the current database can also be used as a single source for identifying most of the mass scale gene expression data as it directs the user to the original data in all cases.
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As expected, the tissue-specific compilation of mass-scale gene expression data resulted in agreement with MCD (individual gene studies, see table 2).
Both features are critically important for increasing the power of identifying co-expressed genes in large scale gene expression datasets.
Large scale gene expression analysis has become an essential tool for many biological and medical investigations.
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.
Large-scale, gene expression profiling methods allow for high throughput analysis of physiological pathways at a fraction of the cost of individual gene expression analysis.
The rice full-length cDNAs also serve as the main resources for large-scale gene expression profiling.
Large-scale gene expression studies frequently employ microarray technologies.
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