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However, a cyclic loess method called fastlo [ 55], originally designed for microarray data, worked well to correct our nonlinear skew.
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SNP, MJL, and AJM carried out the statistical analysis of the microarray data, and worked with KEM and KH in refining gene annotations and expression analysis.
On many occasions, a reduced fragment of microarray data could work more efficiently to reveal more subtle insights into the target biological phenomena than the non-reduced global genome data do [ 22- 24].
Practitioners should thus apply robust approaches for microarray data analysis, which work reliably irrespective of whether noise is Gaussian or heavy tailed.
Microarray data for this work had been deposited in NCBI GEO as GSE41462 (antisense data) and GSE41464 (sense data).
The microarray data from this work was submitted to the NCBI database with Gene Expression Omnibus (GEO) accession number [ GSE11217].
The microarray data from this work was submitted to the ArrayExpress database and the accession number is E-MEXP-1711 E-MEXP-1711 E-MEXP-1711
The microarray data in this work are deposited at GEO (http://www.ncbi.nlm.nih.gov/geo/) with the accession number: GSE40307.
An indirect way of validating our results is to compare this microarray data to previous work from our laboratory showing individual gene expression patterns.
While the major public DNA sequence database groups at the NCBI and EMBL-EBI had developed nascent microarray data repositories, and work was under way to create a similar database at the DDBJ, submitting data to these databases was a considerable burden for authors.
Finally, there is hardly a one-size-fits-all approach to microarray data analysis and what works in one situation may not be universally applicable.
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