Wen-Hsin Tai
YOU?
Author Swipe
View article: Additional file 4 of Evaluating the transcriptional fidelity of cancer models
Additional file 4 of Evaluating the transcriptional fidelity of cancer models Open
Additional file 4: Table S4. Training parameters used to train 16 cross-species classifiers.
View article: Additional file 3 of Evaluating the transcriptional fidelity of cancer models
Additional file 3 of Evaluating the transcriptional fidelity of cancer models Open
Additional file 3: Table S3. Decision thresholds and the corresponding average precision, recall and F1 for the general classifier and subtype classifiers.
View article: Additional file 5 of Evaluating the transcriptional fidelity of cancer models
Additional file 5 of Evaluating the transcriptional fidelity of cancer models Open
Additional file 5: Table S5. Accessions of tumor microarray data used in validation.
View article: Additional file 12 of Evaluating the transcriptional fidelity of cancer models
Additional file 12 of Evaluating the transcriptional fidelity of cancer models Open
Additional file 12: Table S11. General classification profiles of GEMMs.
View article: Additional file 9 of Evaluating the transcriptional fidelity of cancer models
Additional file 9 of Evaluating the transcriptional fidelity of cancer models Open
Additional file 9: Table S8. LUSC CCLs subtype comparison between CCN, Yu et al, Salvadores et al.
View article: Additional file 10 of Evaluating the transcriptional fidelity of cancer models
Additional file 10 of Evaluating the transcriptional fidelity of cancer models Open
Additional file 10: Table S9. General classification profiles of PDXs.
View article: Additional file 1 of Evaluating the transcriptional fidelity of cancer models
Additional file 1 of Evaluating the transcriptional fidelity of cancer models Open
Additional file 1: Table S1. Specific parameters used for the training of all CCN classifiers.
View article: Additional file 14 of Evaluating the transcriptional fidelity of cancer models
Additional file 14 of Evaluating the transcriptional fidelity of cancer models Open
Additional file 14: Table S13. General classification profiles of tumoroids.
View article: Additional file 2 of Evaluating the transcriptional fidelity of cancer models
Additional file 2 of Evaluating the transcriptional fidelity of cancer models Open
Additional file 2: Table S2. Gene-pairs selected for final training of CCN general, subtype classifiers and single-cell classifier.
View article: Additional file 11 of Evaluating the transcriptional fidelity of cancer models
Additional file 11 of Evaluating the transcriptional fidelity of cancer models Open
Additional file 11: Table S10. Subtype classification profiles of PDXs.
View article: Additional file 7 of Evaluating the transcriptional fidelity of cancer models
Additional file 7 of Evaluating the transcriptional fidelity of cancer models Open
Additional file 7: Table S6. General classification profiles of CCLs.
View article: Additional file 13 of Evaluating the transcriptional fidelity of cancer models
Additional file 13 of Evaluating the transcriptional fidelity of cancer models Open
Additional file 13: Table S12. Subtype classification profiles of GEMMs.
View article: Additional file 8 of Evaluating the transcriptional fidelity of cancer models
Additional file 8 of Evaluating the transcriptional fidelity of cancer models Open
Additional file 8: Table S7. Subtype classification profiles of CCLs.
View article: Additional file 15 of Evaluating the transcriptional fidelity of cancer models
Additional file 15 of Evaluating the transcriptional fidelity of cancer models Open
Additional file 15: Table S14. Subtype classification profiles of tumoroids.
View article: Evaluating the transcriptional fidelity of cancer models
Evaluating the transcriptional fidelity of cancer models Open
Background Cancer researchers use cell lines, patient derived xenografts, engineered mice, and tumoroids as models to investigate tumor biology and to identify therapies. The generalizability and power of a model derives from the fidelity …