%0 Journal Article %1 wang2021scgnn %A Wang, Juexin %A Ma, Anjun %A Chang, Yuzhou %A Gong, Jianting %A Jiang, Yuexu %A Qi, Ren %A Wang, Cankun %A Fu, Hongjun %A Ma, Qin %A Xu, Dong %D 2021 %I Nature Publishing Group %J Nature communications %K single cell gnn graph neural network rna seq rna-seq paper:fastcor citedby:scholar:count:3 citedby:scholar:timestamp:2021-6-4 %N 1 %P 1--11 %T scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses %V 12

@article{wang2021scgnn, added-at = {2021-06-04T23:16:37.000+0200}, author = {Wang, Juexin and Ma, Anjun and Chang, Yuzhou and Gong, Jianting and Jiang, Yuexu and Qi, Ren and Wang, Cankun and Fu, Hongjun and Ma, Qin and Xu, Dong}, biburl = {https://www.bibsonomy.org/bibtex/27c7191d3fb7a8bbdf22a57ae0b86886e/becker}, interhash = {ef155f57480a1147397302b4ed39b310}, intrahash = {7c7191d3fb7a8bbdf22a57ae0b86886e}, journal = {Nature communications}, keywords = {single cell gnn graph neural network rna seq rna-seq paper:fastcor citedby:scholar:count:3 citedby:scholar:timestamp:2021-6-4}, number = 1, pages = {1--11}, publisher = {Nature Publishing Group}, timestamp = {2021-06-04T23:16:37.000+0200}, title = {scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses}, volume = 12, year = 2021 }