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The analysis was done on a microarray dataset consisting of 65 prostate cancer and 63 cancer-adjacent control tissue samples from the same patients [37].
The analysis was done on a microarray dataset consisting of 39 paired pancreatic cancer and cancer-adjacent control tissue samples from the same patients [32].
The analysis was done on a microarray dataset consisting of 58 lung cancer tissue and 49 cancer-adjacent control tissue samples from the same patients [26].
The analysis was done on a microarray dataset consisting of 89 stomach cancer and 23 cancer-adjacent control tissues from the same patients [43].
We used a microarray dataset consisting of 22 different murine tissues, with 3 5 replicates for each tissue (in total 70 microarrays).
Our analysis was done on a microarray dataset consisting of 53 colon cancer and 28 cancer-adjacent control tissues from the same patients (some of the cancer samples have no reference samples) [18].
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The microarray dataset consists of N genes and M experiments can be represented as an M*N matrix.
(Additional file 1: Table S3) To further validate the use of these tissue-specific GETs as predictors of normal human tissues, an extensive tissue-prediction analysis was carried out on a total of 61 microarray datasets consisting of 797 samples from 16 different human tissues.
This was accomplished by generating co-expression clusters using LSTs as seeds to cluster other genes from two microarray datasets consisting of transcript profiles from 18 cattle-tissues and liver of animals fed two different diets at several peripartal time-points.
We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD) as a target dataset to evaluate our method.
Because cell type greatly influences expression patterns [ 28], and because our microarray datasets consisted of cell-type-specific batches that confound effects from these two variables, each cell type was treated as a separate dataset for processing.
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