Sentence examples for microarray datasets where from inspiring English sources

The microarray datasets, where the tissue samples represent the samples from cancerous (malignant) and non-cancerous (benign) cells, the classification of them will result in binary cancer classification.

Feature selection is critical when LDA is applied to microarray datasets where the number of genes (p) is distinctly larger than the number of samples (n) because overfitting can easily occur.

Here we verified these data using independent expression microarray datasets where collectively these findings support the general concept that MIF is differentially expressed between non-malignant and malignant lesions with increased expression during melanoma progression.

We also checked enrichment analysis results in the TCGA and EGA microarray datasets, where we found that 24 of the total 28 CEPs (86%) are confirmed by at least one other data set and 15 of them are confirmed in both (Table  3).

We purposefully used a microarray dataset where patients received no adjuvant systemic therapy so as not to confound the survival data with the use of chemotherapeutics or estrogen receptor antagonists.

We further demonstrate the usefulness of our method on a microarray dataset, where we obtain meaningful results.

In order to assess the scalability of our method to higher numbers of variables, we subsequently conducted experiments on publicly available microarray datasets (Table 1) where dimensionality was increased between two and three orders of magnitude compared to the proteomic datasets described above.

We illustrate it via simulations and in a collection of eight estrogen-receptor positive breast cancer microarray gene-expression datasets, where the objective is predicting distant metastasis-free survival (DMFS).

FABIA was tested on three microarray datasets with known subclusters, where it was two times the best and once the second best method among the compared biclustering approaches.

For the most part, the studies that have tried to do large‐scale gene prediction assignment have used well‐known model organisms where large microarray datasets were available.

Microarray datasets were preprocessed by RMA and, where applicable, corrected for batch effects by ComBat, as described in Methods.