409 results on '"Clauser, Karl R."'
Search Results
2. Translation of non-canonical open reading frames as a cancer cell survival mechanism in childhood medulloblastoma
3. Workflow enabling deepscale immunopeptidome, proteome, ubiquitylome, phosphoproteome, and acetylome analyses of sample-limited tissues
4. Deep learning integrates histopathology and proteogenomics at a pan-cancer level
5. Pan-cancer analysis of post-translational modifications reveals shared patterns of protein regulation
6. Pan-cancer proteogenomics connects oncogenic drivers to functional states
7. Proteogenomic data and resources for pan-cancer analysis
8. HLA-II immunopeptidome profiling and deep learning reveal features of antigenicity to inform antigen discovery
9. Sensitive, High-Throughput HLA-I and HLA-II Immunopeptidomics Using Parallel Accumulation-Serial Fragmentation Mass Spectrometry
10. What Can Ribo-Seq, Immunopeptidomics, and Proteomics Tell Us About the Noncanonical Proteome?
11. Cancer proteogenomics: current impact and future prospects
12. Unannotated proteins expand the MHC-I-restricted immunopeptidome in cancer
13. Profiling SARS-CoV-2 HLA-I peptidome reveals T cell epitopes from out-of-frame ORFs
14. Site-specific identification and quantitation of endogenous SUMO modifications under native conditions.
15. Phenotype, specificity and avidity of antitumour CD8+ T cells in melanoma
16. Noncanonical open reading frames encode functional proteins essential for cancer cell survival
17. Abstract A038: Non-canonical MHC class I-associated antigens in pancreatic cancer
18. Glyco-centric lectin magnetic bead array (LeMBA) - proteomics dataset of human serum samples from healthy, Barrett׳s esophagus and esophageal adenocarcinoma individuals.
19. Proteomic analyses of ECM during pancreatic ductal adenocarcinoma progression reveal different contributions by tumor and stromal cells
20. A large peptidome dataset improves HLA class I epitope prediction across most of the human population
21. PANOPLY: a cloud-based platform for automated and reproducible proteogenomic data analysis
22. Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics
23. Suppression of pancreatic ductal adenocarcinoma growth and metastasis by fibrillar collagens produced selectively by tumor cells
24. Author Correction: Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics
25. 1040-B Identifying tumor-associated antigens in different stages of multiple myeloma using low input HLA immunopeptidomics
26. Pan-viral ORFs discovery using Massively Parallel Ribosome Profiling
27. Deep learning integrates histopathology and proteogenomics at a pan-cancer level
28. Pan-cancer proteogenomics connects oncogenic drivers to functional states
29. Pan-cancer analysis of post-translational modifications reveals shared patterns of protein regulation
30. Proteogenomic data and resources for pan-cancer analysis
31. Plk1 self-organization and priming phosphorylation of HsCYK-4 at the spindle midzone regulate the onset of division in human cells.
32. Microscaled proteogenomic methods for precision oncology
33. Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography–mass spectrometry
34. Figure 1 from Proteomic Profiling of Extracellular Matrix Components from Patient Metastases Identifies Consistently Elevated Proteins for Developing Nanobodies That Target Primary Tumors and Metastases
35. Supplementary Data from Proteomic Profiling of Extracellular Matrix Components from Patient Metastases Identifies Consistently Elevated Proteins for Developing Nanobodies That Target Primary Tumors and Metastases
36. The HLA-II immunopeptidome of SARS-CoV-2
37. What can Ribo-seq and proteomics tell us about the non-canonical proteome?
38. Translation of non-canonical open reading frames as a cancer cell survival mechanism in childhood medulloblastoma
39. Proteomic profiling of extracellular matrix components from patient metastases identifies consistently elevated proteins for developing nanobodies that target primary tumors and metastases
40. The HLA-II immunopeptidome of SARS-CoV-2
41. Supplementary File 5 from Mass Spectrometry–Based Proteomics Reveals Potential Roles of NEK9 and MAP2K4 in Resistance to PI3K Inhibition in Triple-Negative Breast Cancers
42. Supplementary File 1 from Mass Spectrometry–Based Proteomics Reveals Potential Roles of NEK9 and MAP2K4 in Resistance to PI3K Inhibition in Triple-Negative Breast Cancers
43. Supplementary File 2 from Mass Spectrometry–Based Proteomics Reveals Potential Roles of NEK9 and MAP2K4 in Resistance to PI3K Inhibition in Triple-Negative Breast Cancers
44. Extended Methods from Mass Spectrometry–Based Proteomics Reveals Potential Roles of NEK9 and MAP2K4 in Resistance to PI3K Inhibition in Triple-Negative Breast Cancers
45. Supplementary File 3 from Mass Spectrometry–Based Proteomics Reveals Potential Roles of NEK9 and MAP2K4 in Resistance to PI3K Inhibition in Triple-Negative Breast Cancers
46. Supplementary Figures from Mass Spectrometry–Based Proteomics Reveals Potential Roles of NEK9 and MAP2K4 in Resistance to PI3K Inhibition in Triple-Negative Breast Cancers
47. Supplementary tables 1 and 2 from Mass Spectrometry–Based Proteomics Reveals Potential Roles of NEK9 and MAP2K4 in Resistance to PI3K Inhibition in Triple-Negative Breast Cancers
48. Data from Mass Spectrometry–Based Proteomics Reveals Potential Roles of NEK9 and MAP2K4 in Resistance to PI3K Inhibition in Triple-Negative Breast Cancers
49. Supplementary File 4 from Mass Spectrometry–Based Proteomics Reveals Potential Roles of NEK9 and MAP2K4 in Resistance to PI3K Inhibition in Triple-Negative Breast Cancers
50. Sensitive, high-throughput HLA-I and HLA-II immunopeptidomics using parallel accumulation-serial fragmentation mass spectrometry
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