Supervised Adversarial Alignment of Single-Cell RNA-seq Data is a research paper published in Journal of Computational Biology (2021). On theSindex it has a DataRank of 0. It has been cited 35 times.
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NIDA NIH HHS
Grant: P30 DA035778
NIGMS NIH HHS
Grant: R01 GM093156
NIH HHS
Grant: OT2 OD026682
NIGMS NIH HHS
Grant: R01 GM122096
FWCI
2.46
Citation Percentile
0.9%
Influential Citations
1
Citation Trend
Fields of Study
MeSH Terms
Keywords
Additional file 1 of Identifying tumor cells at the single-cell level using machine learning
Additional file 1 of Identifying tumor cells at the single-cell level using machine learning
Additional file 4 of Identifying tumor cells at the single-cell level using machine learning
Additional file 4 of Identifying tumor cells at the single-cell level using machine learning
Additional file 1 of Single-cell multi-omics integration for unpaired data by a siamese network with graph-based contrastive loss
Additional file 1 of Single-cell multi-omics integration for unpaired data by a siamese network with graph-based contrastive loss
Additional file 2 of Single-cell multi-omics integration for unpaired data by a siamese network with graph-based contrastive loss
Additional file 2 of Single-cell multi-omics integration for unpaired data by a siamese network with graph-based contrastive loss