Protein Imputation
Cross-source consensus on Protein Imputation from 1 sources and 7 claims.
1 sources · 7 claims
Uses
How it works
Benefits
Comparisons
Evidence quality
Highlighted claims
- In the Levine masked-protein benchmark, scpFormer achieved mean Pearson correlation 0.751, outperforming random forest, k-nearest neighbors, and linear regression. — scpFormer: A Foundation Model for Unified Representation and Integration of the Single-Cell Proteomics
- The MIS-C zero-shot experiment used GSE166489 without dataset-specific fine-tuning. — scpFormer: A Foundation Model for Unified Representation and Integration of the Single-Cell Proteomics
- For FLT3 imputation, scpFormer reached correlation 0.452 while conventional methods were near 0.20. — scpFormer: A Foundation Model for Unified Representation and Integration of the Single-Cell Proteomics
- For imputation, scpFormer adapts the same self-decoder objective used during pretraining to reconstruct missing protein expression values. — scpFormer: A Foundation Model for Unified Representation and Integration of the Single-Cell Proteomics
- Because naturally missing proteins lacked ground truth, zero-shot clinical imputation was evaluated through topology and biological manifold recovery with scIB. — scpFormer: A Foundation Model for Unified Representation and Integration of the Single-Cell Proteomics
- scpFormer's zero-shot imputation on MIS-C produced the best biological conservation and clustering agreement among baseline imputation strategies. — scpFormer: A Foundation Model for Unified Representation and Integration of the Single-Cell Proteomics
- scpFormer had its largest qualitative imputation advantage for low-abundance or nonlinear markers. — scpFormer: A Foundation Model for Unified Representation and Integration of the Single-Cell Proteomics