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Recently, there have been improvements in the microarray field, and more microarray datasets are available for meta-analysis.
As more and more microarray datasets and technologies development, they have gradually become standard resources and tools to analysis complex disease.
As more and more microarray datasets on the same species are produced from different laboratories, their integration leads to more robust and more reliable results.
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A more diverse microarray dataset of BA22, BA10, BA46 samples and other tissue specific samples from schizophrenic patients could be compared and analyzed for the future study.
More high-quality microarray datasets will be included into the database continually.
Additionally, the expression correlation values of h2h pairs calculated across more than 100 microarray datasets enable a second-order correlation analysis of h2h pairs (15), through which we may get closer to deciphering the transcription regulation mechanism of h2h organization.
Since genes are assumed to be independent, these datasets are more ideal than real microarray datasets.
Oncomine [13] was chosen because it is a public cancer microarray platform incorporating 264 independent microarray datasets, totaling more than 18,000 microarray experiments, which span 35 cancer types.
Experiments showed that coexpression patterns mined from multiple independent microarray datasets are more likely to be functionally relevant and thus improve gene function predictions [ 1].
However, we are currently carrying out additional RT-PCR experiments with other microarray datasets to achieve more representative estimates.
Using a novel 3D network-based approach we have analysed six large human cancer microarray datasets derived from more than 1,000 individuals.
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