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1. Author Correction: Pan-cancer analysis of whole genomes

2. Pan-cancer analysis of whole genomes

3. A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns

5. Table S15 from Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data

6. Figure S5: GDD-ENS Performance Across Purity Values from Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data

7. Figure S3: Confusion Matrix Across All Confidence Predictions from Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data

8. Figure S4: Ancestry Accuracy Differentials from Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data

9. Figure S6: Individual Type Shapley Values from Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data

10. Figure S1: Accuracy of Feature-Specific Classifiers from Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data

11. Figure S2: GDD-ENS Precision Recall Curves from Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data

12. Figure S7: Individual Type Shapley Values - Broad Categories from Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data

13. Supplementary Methods from Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data

14. Figure S8: Organ Shapley Value Distributions from Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data

15. Figure S11: Heatmap of Labels Mapped for Adaptable Prior Distributions from Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data

16. Figure S12: Results flow for Met Site, Histology Prior from Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data

17. Figure S10: KRAS Shapley Values across typesSupplementary Data from Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data

18. Deep Learning Model for Tumor Type Prediction using Targeted Clinical Genomic Sequencing Data

19. ASCL1 Reorganizes Chromatin to Direct Neuronal Fate and Suppress Tumorigenicity of Glioblastoma Stem Cells

20. Deep Learning Model for Tumor Type Prediction using Targeted Clinical Genomic Sequencing Data

23. A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns

24. Combined burden and functional impact tests for cancer driver discovery using DriverPower

25. Integrative pathway enrichment analysis of multivariate omics data

26. Pathway and network analysis of more than 2500 whole cancer genomes

27. Divergent mutational processes distinguish hypoxic and normoxic tumours

28. Genomic footprints of activated telomere maintenance mechanisms in cancer

29. Author Correction: A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns (Nature Communications, (2020), 11, 1, (728), 10.1038/s41467-019-13825-8)

32. Pan-cancer analysis of whole genomes

34. Sex differences in oncogenic mutational processes

35. A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns

36. Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

37. Sex differences in oncogenic mutational processes

38. Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

39. Pan-cancer analysis of whole genomes

40. Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

41. Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

42. A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns

43. A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns

44. Deep Learning Model for Tumor Type Prediction using Targeted Clinical Genomic Sequencing Data.

45. Genome Gerrymandering: optimal division of the genome into regions with cancer type specific differences in mutation rates.

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