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In the present study, on the basis of multiple goldfish microarray datasets, we used comprehensive normalizations, differential gene expression identification, multivariate and gene ontology (GO) analyses to characterize the season-related expression patterns in the female goldfish brain.
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In the present study, by analyzing multiple female goldfish brain microarray datasets, we have characterized global gene expression patterns for a seasonal cycle.
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.
Microarray datasets of gene expression in female goldfish hypothalamus and telencephalon have been collected to represent four seasonal time points.
All experiments were based on goldfish sampled at different seasonal time points; thus, these microarray datasets contain important seasonal gene expression information (Table S1).
A scalable method for integration and functional analysis of multiple microarray datasets.
This enrichment was further validated in the independent microarray datasets (Supplementary Fig. 25).
A snapshot of gene expression signatures generated using microarray datasets associated with excessive scarring.
Meta-analysis of public microarray datasets reveals voltage-gated calcium gene signatures in clinical cancer patients.
To this end, we first analyzed microarray datasets in silico to investigate the expression changes of HOPX in IPF lungs.
The microarray datasets generated during the current study are available in the NCBI GEO database (accession no. GSE108648).
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