MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits is a research paper published in Genome biology (2021). On theSindex it has a DataRank of 0. It has been cited 82 times.
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NCI NIH HHS
Grant: U10 CA180794
NIGMS NIH HHS
Grant: P20 GM109035
FWCI
7.63
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Additional file 1 of MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits
Additional file 1 of MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits
Additional file 2 of MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits
Additional file 2 of MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits
Additional file 1 of Regularized multi-trait multi-locus linear mixed models for genome-wide association studies and genomic selection in crops
Additional file 1 of Regularized multi-trait multi-locus linear mixed models for genome-wide association studies and genomic selection in crops
Additional file 2 of Regularized multi-trait multi-locus linear mixed models for genome-wide association studies and genomic selection in crops
Additional file 2 of Regularized multi-trait multi-locus linear mixed models for genome-wide association studies and genomic selection in crops
Additional file 1 of Improving the accuracy of genomic prediction in dairy cattle using the biologically annotated neural networks framework
Additional file 1 of Improving the accuracy of genomic prediction in dairy cattle using the biologically annotated neural networks framework