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Richard W Shuai

Showing results (1-10 of 9) with videos related to

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Cell Systems|November 1, 2023
IgLM: Infilling language modeling for antibody sequence designRichard W Shuai, Jeffrey A Ruffolo, Jeffrey J Gray
Proceedings of Machine Learning Research|November 27, 2025
Sidechain conditioning and modeling for full-atom protein sequence design with FAMPNNTalal Widatalla, Richard W Shuai, Brian L Hie, et al.
Biorxiv : the Preprint Server for Biology|November 19, 2025
Ensemble-conditioned protein sequence design with CalibyRichard W Shuai, Tianyu Lu, Subhang Bhatti, et al.
Biorxiv : the Preprint Server for Biology|January 8, 2024
Characterizing uncertainty in predictions of genomic sequence-to-activity modelsAyesha Bajwa, Ruchir Rastogi, Pooja Kathail, et al.
Nature Genetics|November 30, 2023
Personal transcriptome variation is poorly explained by current genomic deep learning modelsConnie Huang, Richard W Shuai, Parth Baokar, et al.
Genome Biology|August 1, 2024
Current genomic deep learning models display decreased performance in cell type-specific accessible regionsPooja Kathail, Richard W Shuai, Ryan Chung, et al.
Biorxiv : the Preprint Server for Biology|July 19, 2024
Current genomic deep learning models display decreased performance in cell type specific accessible regionsPooja Kathail, Richard W Shuai, Ryan Chung, et al.
Proceedings of the National Academy of Sciences of the United States of America|June 25, 2024
An all-atom protein generative modelAlexander E Chu, Jinho Kim, Lucy Cheng, et al.
Nature Genetics|September 10, 2024
Variants in tubule epithelial regulatory elements mediate most heritable differences in human kidney functionGabriel B Loeb, Pooja Kathail, Richard W Shuai, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
Cell Systems|November 1, 2023
IgLM: Infilling language modeling for antibody sequence designRichard W Shuai, Jeffrey A Ruffolo, Jeffrey J Gray
Proceedings of Machine Learning Research|November 27, 2025
Sidechain conditioning and modeling for full-atom protein sequence design with FAMPNNTalal Widatalla, Richard W Shuai, Brian L Hie, et al.
Biorxiv : the Preprint Server for Biology|November 19, 2025
Ensemble-conditioned protein sequence design with CalibyRichard W Shuai, Tianyu Lu, Subhang Bhatti, et al.
Biorxiv : the Preprint Server for Biology|January 8, 2024
Characterizing uncertainty in predictions of genomic sequence-to-activity modelsAyesha Bajwa, Ruchir Rastogi, Pooja Kathail, et al.
Nature Genetics|November 30, 2023
Personal transcriptome variation is poorly explained by current genomic deep learning modelsConnie Huang, Richard W Shuai, Parth Baokar, et al.
Genome Biology|August 1, 2024
Current genomic deep learning models display decreased performance in cell type-specific accessible regionsPooja Kathail, Richard W Shuai, Ryan Chung, et al.
Biorxiv : the Preprint Server for Biology|July 19, 2024
Current genomic deep learning models display decreased performance in cell type specific accessible regionsPooja Kathail, Richard W Shuai, Ryan Chung, et al.
Proceedings of the National Academy of Sciences of the United States of America|June 25, 2024
An all-atom protein generative modelAlexander E Chu, Jinho Kim, Lucy Cheng, et al.
Nature Genetics|September 10, 2024
Variants in tubule epithelial regulatory elements mediate most heritable differences in human kidney functionGabriel B Loeb, Pooja Kathail, Richard W Shuai, et al.
Pageof 1