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The generation and analysis of the microarray dataset using skin biopsies from patients with SSc and normal controls was described previously [25].
Classify microarray dataset using SVM.
(1) Preprocess microarray dataset using mRMR filtering method.
(6) Memorize the best food source found so far Until requirements are met (7) Classify microarray dataset using SVM classifier.
Similarly we analysed the microarray dataset using probe annotations based on v1.0 and v2.0 of the genome and assembly with similar gene-level results in both cases.
We generated a microarray dataset using rat VF mucosae harvested at three post-injury (PI) time points, and VF mucosae from experimentally naïve age-matched controls (Fig. 1).
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The microarray dataset used in this manuscript has been deposited with the Gene Expression Omnibus (GEO) and assigned the following GEO accession number: GSE62180.
However, as the cutoff value of 200 was determined based on negative controls on the chip to match some optimum ratio of FN to FP, and to our knowledge, almost all published works utilizing this microarray dataset used the cutoff 200, a liberty given to the lower cutoff value for reducing FN should not be encouraged.
We performed experiments on 12 highly imbalanced microarray datasets using linear and Gaussian kernel, achieving the highest average predictive performance with our approach compared with the most well-known feature selection strategies.
The purpose of this study was to extract and evaluate such seasonal gene expression characteristics from the existing multiple microarray datasets using the goldfish model.
Six additional microarray datasets using cDNA, oligo, or Affymetrix arrays are available through Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/sites/entrez?db=geo) and citations for publications are also linked to these public data sets [33], [35], [36].
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