Drug Response Prediction
Cross-source consensus on Drug Response Prediction from 1 sources and 6 claims.
1 sources · 6 claims
Uses
Benefits
Comparisons
Evidence quality
Highlighted claims
- At the global cell-line and drug prediction level, scpFormer achieved Pearson correlation 0.921, compared with 0.888 for scFoundation. — scpFormer: A Foundation Model for Unified Representation and Integration of the Single-Cell Proteomics
- Among 223 tested drugs, 183 had lower mean absolute error with scpFormer than with scFoundation. — scpFormer: A Foundation Model for Unified Representation and Integration of the Single-Cell Proteomics
- Drug response prediction performance was evaluated with Pearson correlation between predicted and true IC50 and mean absolute error across drugs. — scpFormer: A Foundation Model for Unified Representation and Integration of the Single-Cell Proteomics
- scpFormer embeddings were used in DeepCDR for cancer drug response prediction from paired cancer cell-line proteomic profiles and IC50 data. — scpFormer: A Foundation Model for Unified Representation and Integration of the Single-Cell Proteomics
- Pathway-level stratification showed gains for scpFormer across nearly all drug pathway categories. — scpFormer: A Foundation Model for Unified Representation and Integration of the Single-Cell Proteomics
- The paper interprets improved IC50 prediction as evidence that scpFormer embeddings capture cellular states related to pharmacological response. — scpFormer: A Foundation Model for Unified Representation and Integration of the Single-Cell Proteomics