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1. Genetic control of DNA methylation is largely shared across European and East Asian populations

2. Impact of humid climate on rheumatoid arthritis faecal microbiome and metabolites

3. Learning functional conservation between human and pig to decipher evolutionary mechanisms underlying gene expression and complex traits

4. Improved analyses of GWAS summary statistics by reducing data heterogeneity and errors

5. Mechanism of interventional effect and targets of Zhuyu pill in regulating and suppressing colitis and cholestasis

6. Phenotypic covariance across the entire spectrum of relatedness for 86 billion pairs of individuals

7. Widespread signatures of natural selection across human complex traits and functional genomic categories

8. Promoter-anchored chromatin interactions predicted from genetic analysis of epigenomic data

9. Direct identification of neoantigen-specific TCRs from tumor specimens by high-throughput single-cell sequencing

10. Improved polygenic prediction by Bayesian multiple regression on summary statistics

11. Genome-wide association study of medication-use and associated disease in the UK Biobank

12. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes

13. Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood

14. Global genetic differentiation of complex traits shaped by natural selection in humans

15. Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits

16. Causal associations between risk factors and common diseases inferred from GWAS summary data

17. Quantifying the mapping precision of genome-wide association studies using whole-genome sequencing data

18. A Method for Real-Time Monitoring of Inherent System Loss Designed for FLRDS-Based Gas Sensors

19. Development of a T cell receptor targeting an HLA-A*0201 restricted epitope from the cancer-testis antigen SSX2 for adoptive immunotherapy of cancer.

20. Q Vectors, Bicistronic Retroviral Vectors for Gene Transfer

22. Adoptive Cellular Therapy with Autologous Tumor-Infiltrating Lymphocytes and T-cell Receptor–Engineered T Cells Targeting Common p53 Neoantigens in Human Solid Tumors

23. Supplementary Figure from Adoptive Cellular Therapy with Autologous Tumor-Infiltrating Lymphocytes and T-cell Receptor–Engineered T Cells Targeting Common p53 Neoantigens in Human Solid Tumors

24. Data from Adoptive Cellular Therapy with Autologous Tumor-Infiltrating Lymphocytes and T-cell Receptor–Engineered T Cells Targeting Common p53 Neoantigens in Human Solid Tumors

25. Data from Single-Cell Transcriptome Analysis Reveals Gene Signatures Associated with T-cell Persistence Following Adoptive Cell Therapy

28. Supplemental figure 2 from Tumor-Infiltrating Lymphocytes Genetically Engineered with an Inducible Gene Encoding Interleukin-12 for the Immunotherapy of Metastatic Melanoma

29. Supplemental Figure 2 from Characterization of an Immunogenic Mutation in a Patient with Metastatic Triple-Negative Breast Cancer

32. Supplemental figure 4 from Tumor-Infiltrating Lymphocytes Genetically Engineered with an Inducible Gene Encoding Interleukin-12 for the Immunotherapy of Metastatic Melanoma

35. Supplemental Table 1 from Characterization of an Immunogenic Mutation in a Patient with Metastatic Triple-Negative Breast Cancer

36. Data from Development of Human Anti-Murine T-Cell Receptor Antibodies in Both Responding and Nonresponding Patients Enrolled in TCR Gene Therapy Trials

37. Supplemental Table 3 from Characterization of an Immunogenic Mutation in a Patient with Metastatic Triple-Negative Breast Cancer

38. Data from Tumor Mutational Burden Is Polygenic and Genetically Associated with Complex Traits and Diseases

40. supplemental figure legend from Tumor-Infiltrating Lymphocytes Genetically Engineered with an Inducible Gene Encoding Interleukin-12 for the Immunotherapy of Metastatic Melanoma

41. Supplemental Figure 3 from Characterization of an Immunogenic Mutation in a Patient with Metastatic Triple-Negative Breast Cancer

42. Supplemental Figure 1 from Characterization of an Immunogenic Mutation in a Patient with Metastatic Triple-Negative Breast Cancer

43. Data from Gene Expression Profiling using Nanostring Digital RNA Counting to Identify Potential Target Antigens for Melanoma Immunotherapy

44. Supplementary Figure 2 from Gene Expression Profiling using Nanostring Digital RNA Counting to Identify Potential Target Antigens for Melanoma Immunotherapy

45. Supplemental Table 2 from Characterization of an Immunogenic Mutation in a Patient with Metastatic Triple-Negative Breast Cancer

47. Data from Characterization of an Immunogenic Mutation in a Patient with Metastatic Triple-Negative Breast Cancer

48. Supplemental figure 5 from Tumor-Infiltrating Lymphocytes Genetically Engineered with an Inducible Gene Encoding Interleukin-12 for the Immunotherapy of Metastatic Melanoma

49. Supplemental figure 1 from Tumor-Infiltrating Lymphocytes Genetically Engineered with an Inducible Gene Encoding Interleukin-12 for the Immunotherapy of Metastatic Melanoma

50. Data from Tumor-Infiltrating Lymphocytes Genetically Engineered with an Inducible Gene Encoding Interleukin-12 for the Immunotherapy of Metastatic Melanoma

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