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Bimodal genes in the murine microarray data (Table 1) were identified using a statistical method applied to bimodality in glucose distribution [ 46, 47].
For this reason, the present study focuses on murine microarray data containing approximately 400 samples, all obtained using the Affymetrix MG-U74Av2 platform (Table 1) [ 3, 6, 25- 39].
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Murine gene expression microarray data sets were generated in our previous studies (Samstein et al., 2012; Arvey et al., 2014).
In other experiments involving the repression of miRNA functionality using 'antagomirs' [5] and miR-155 deficient mice, DIANA-mirExTra has correctly identified the repressed murine miRNA in both occasions (mmu-miR-122a and mmu-miR-155) using microarray data.
In order to analyze the main reasons resulting in the heterogeneous immunoreactivity patterns, we further evaluated the differences in theoretical expression abundance and experimental gene expression profiling between the human and murine immunoreactive proteins of B. p using the JCAT tool and DNA microarray data (Shown in Fig. 3).
For this study, we created a new murine genomic resource of 73 mammary tumors profiled by both gene expression and DNA copy number microarray data (GSE52173); this new resource complements our human data set that contains 644 human breast tumors that have both gene expression and DNA copy number data (GSE52173 and http://tcga-data.nci.nih.gov/tcga).
In order to study whether the candidate genes are regulated by osteogenic molecule (bmp2) in murine MC3T3-1b pre-osteoblast celineine, we performed in silico gene expression study by retrieving the normalized microarray data [Accession number: GDS679) (Zamurovic et al. 2004)] from GEO database.
The authors are grateful to Prof. Fritz von Weizsäcker and Sabine MacNelly, Department of Internal Medicine II, University of Freiburg, D-79104 Freiburg, Germany for the preparation of primary murine hepatocytes and to Dr. Carsten Sticht, Medical Research Center, University Hospital Mannheim, D-68135 Mannheim, Germany for helpful discussions and uploading the microarray data into the GEO database.
TK and RNI analyzed the microarray data.
Thus, understanding microarray data processing steps becomes critical for performing optimal microarray data analysis.
Steps 3.3 and 3.4 extracted drug microarray and disease microarray data respectively.
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