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Methods in Molecular Biology (Clifton, N.J.)
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March 4, 2020
OpenVax: An Open-Source Computational Pipeline for Cancer Neoantigen Prediction
Julia Kodysh, Alex Rubinsteyn
Methods in Molecular Biology (Clifton, N.J.)
|
March 4, 2020
High-Throughput MHC I Ligand Prediction Using MHCflurry
Timothy O'Donnell, Alex Rubinsteyn
Cell Systems
|
July 27, 2020
MHCflurry 2.0: Improved Pan-Allele Prediction of MHC Class I-Presented Peptides by Incorporating Antigen Processing
Timothy J O'Donnell, Alex Rubinsteyn, Uri Laserson
Cell Systems
|
October 22, 2020
MHCflurry 2.0: Improved Pan-Allele Prediction of MHC Class I-Presented Peptides by Incorporating Antigen Processing
Timothy J O'Donnell, Alex Rubinsteyn, Uri Laserson
Oncology (Williston Park, N.Y.)
|
December 18, 2015
Mutation-Derived Tumor Antigens: Novel Targets in Cancer Immunotherapy
John P Finnigan, Alex Rubinsteyn, Jeffrey Hammerbacher, et al.
Progress in Molecular Biology and Translational Science
|
August 7, 2019
Bioinformatic methods for cancer neoantigen prediction
Sebastian Boegel, John C Castle, Julia Kodysh, et al.
Briefings in Bioinformatics
|
January 5, 2024
ACE configurator for ELISpot: optimizing combinatorial design of pooled ELISpot assays with an epitope similarity model
Jin Seok Lee, Dhuvarakesh Karthikeyan, Misha Fini, et al.
Cell Systems
|
July 2, 2018
MHCflurry: Open-Source Class I MHC Binding Affinity Prediction
Timothy J O'Donnell, Alex Rubinsteyn, Maria Bonsack, et al.
Nature Machine Intelligence
|
September 29, 2025
Conditional generation of real antigen-specific T cell receptor sequences
Dhuvarakesh Karthikeyan, Sarah N Bennett, Amy G Reynolds, et al.
Frontiers in Immunology
|
February 7, 2018
Computational Pipeline for the PGV-001 Neoantigen Vaccine Trial
Alex Rubinsteyn, Julia Kodysh, Isaac Hodes, et al.
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of 2
Search research articles
Search
Showing results (1-10 of 20) with videos related to
Sort By:
Page
of 2
Methods in Molecular Biology (Clifton, N.J.)
|
March 4, 2020
OpenVax: An Open-Source Computational Pipeline for Cancer Neoantigen Prediction
Julia Kodysh, Alex Rubinsteyn
Methods in Molecular Biology (Clifton, N.J.)
|
March 4, 2020
High-Throughput MHC I Ligand Prediction Using MHCflurry
Timothy O'Donnell, Alex Rubinsteyn
Cell Systems
|
July 27, 2020
MHCflurry 2.0: Improved Pan-Allele Prediction of MHC Class I-Presented Peptides by Incorporating Antigen Processing
Timothy J O'Donnell, Alex Rubinsteyn, Uri Laserson
Cell Systems
|
October 22, 2020
MHCflurry 2.0: Improved Pan-Allele Prediction of MHC Class I-Presented Peptides by Incorporating Antigen Processing
Timothy J O'Donnell, Alex Rubinsteyn, Uri Laserson
Oncology (Williston Park, N.Y.)
|
December 18, 2015
Mutation-Derived Tumor Antigens: Novel Targets in Cancer Immunotherapy
John P Finnigan, Alex Rubinsteyn, Jeffrey Hammerbacher, et al.
Progress in Molecular Biology and Translational Science
|
August 7, 2019
Bioinformatic methods for cancer neoantigen prediction
Sebastian Boegel, John C Castle, Julia Kodysh, et al.
Briefings in Bioinformatics
|
January 5, 2024
ACE configurator for ELISpot: optimizing combinatorial design of pooled ELISpot assays with an epitope similarity model
Jin Seok Lee, Dhuvarakesh Karthikeyan, Misha Fini, et al.
Cell Systems
|
July 2, 2018
MHCflurry: Open-Source Class I MHC Binding Affinity Prediction
Timothy J O'Donnell, Alex Rubinsteyn, Maria Bonsack, et al.
Nature Machine Intelligence
|
September 29, 2025
Conditional generation of real antigen-specific T cell receptor sequences
Dhuvarakesh Karthikeyan, Sarah N Bennett, Amy G Reynolds, et al.
Frontiers in Immunology
|
February 7, 2018
Computational Pipeline for the PGV-001 Neoantigen Vaccine Trial
Alex Rubinsteyn, Julia Kodysh, Isaac Hodes, et al.
Page
of 2