{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["43"],"submitter":["Rahmatzadeh M"],"pubmed_abstract":["Milk yield is most important economic trait in dairy cows and understanding molecular basis and components involved in high-yield production is one of crucial steps to develop and select new breeds. In this study, we used combination of two statistical methods based on the <i>p-value</i> and effect sized to meta-analysis three datasets followed with construction of weighted gene co-expression network based on the expression matrix of differentially expressed genes identified in meta-analysis to detect major gene modules and hub genes. Based on the FDR cut-off<0.05 and Log<sub>2</sub> fold change>2 and < 0.5, we identified 1028 differentially expressed genes that were shared between the Fisher and REM method and were consistent across datasets. Molecular function analysis showed that upregu"],"journal":["Biochemistry and biophysics reports"],"pagination":["102090"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12420518"],"repository":["biostudies-literature"],"pubmed_title":["Identification and characterization of gene networks and key genes related to the high-yield production of milk in high-yield cows using meta-analysis of microarray data."],"pmcid":["PMC12420518"],"pubmed_authors":["Derakhti-Dizaji M","Mahmoudi B","Bazzaz A","Shokri-Gharelo R","Rahmatzadeh M"],"additional_accession":[]},"is_claimable":false,"name":"Identification and characterization of gene networks and key genes related to the high-yield production of milk in high-yield cows using meta-analysis of microarray data.","description":"Milk yield is most important economic trait in dairy cows and understanding molecular basis and components involved in high-yield production is one of crucial steps to develop and select new breeds. In this study, we used combination of two statistical methods based on the <i>p-value</i> and effect sized to meta-analysis three datasets followed with construction of weighted gene co-expression network based on the expression matrix of differentially expressed genes identified in meta-analysis to detect major gene modules and hub genes. Based on the FDR cut-off<0.05 and Log<sub>2</sub> fold change>2 and < 0.5, we identified 1028 differentially expressed genes that were shared between the Fisher and REM method and were consistent across datasets. Molecular function analysis showed that upregu","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Sep","modification":"2026-06-02T20:27:05.643Z","creation":"2026-04-20T03:10:17.006Z"},"accession":"S-EPMC12420518","cross_references":{"pubmed":["40937325"],"doi":["10.1016/j.bbrep.2025.102090"]}}