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We have investigated the validity of the correspondence between the physiological parameter extracted from the regions defined on the common space and those defined on the subject space for brain datasets obtained from a cohort consisting of cognitively normal subjects and patients with dementia or mild cognitive impairment, having moderate to severe cortical degeneration [46].
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The current version of the PD_NGSAtlas is the first release of our database, and it contains next-generation sequencing DNA methylation and gene expression profiles of datasets obtained from human brain and blood samples.
We apply the proposed approach to the automatic segmentation of the human brain from two popular benchmark MR datasets: the simulated BrainWeb MR datasets[15], and normal real MR datasets obtained from the Internet Brain Segmentation Repository (IBSR)[16].
Datasets obtained from large-scale genomic studies now make this possible.
HPM datasets was obtained from our work and other datasets obtained from ProLinks database [ 13].
Microarray gene expression datasets obtained from the Gene Expression Omnibus.
PCA was used to process the huge dataset obtained from this NMR approach.
Each dataset obtained from GEO was reanalyzed using SAM 2.0.
To demonstrate this, we analyzed an in-house RNA-seq dataset (unpublished) from human postmortem brain tissue obtained from two individuals with schizophrenia: one sample has an acceptable RIN (=7.8) and the other sample has a very low RIN (=3).
In our ongoing work we have produced 11 450K datasets comprising samples from whole blood and four brain regions obtained from over 150 different individuals (total n=696).
The human brain tissue was obtained from The Netherlands Brain Bank, Netherlands Institute for Neuroscience, Amsterdam.
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