Richard W. Shuai

Also affiliated with Stanford University

Biochemistry, Genetics and Molecular BiologyMedicine

Pilot corpus only

This score is computed over theSindex pilot corpus and does not cover the full scientific literature. Scores are relative to papers we have ingested — papers, authors, and institutions outside the pilot are not represented. Methodology.

Top 100%percentile

0Author DataRank

Indexed papers

0in pilot corpus

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Papers

Driven by 10 papers. Top paper: “Deep learning for fast spatially varying deconvolution”.

Deep learning for fast spatially varying deconvolution

Optica(2021)10.1364/optica.442438

N/A

0DataRank · unranked

128 citations

Kristina Monakhova, Richard W. Shuai, Laura Waller, Kyrollos Yanny

Generative language modeling for antibody design

bioRxiv (Cold Spring Harbor Laboratory)(2021)10.1101/2021.12.13.472419

N/A

0DataRank · unranked

48 citations

Jeffrey A. Ruffolo, Jeffrey J. Gray, Richard W. Shuai

IgLM: Infilling language modeling for antibody sequence design

Cell Systems(2023)10.1016/j.cels.2023.10.001

N/A

0DataRank · unranked

128 citations

Jeffrey A. Ruffolo, Jeffrey J. Gray, Richard W. Shuai

Personal transcriptome variation is poorly explained by current genomic deep learning models

Nature Genetics(2023)10.1038/s41588-023-01574-w

N/A

0DataRank · unranked

110 citations

Richard W. Shuai, Parth Baokar, Ryan Chung, Ruchir Rastogi, Pooja Kathail +2 more

Personal transcriptome variation is poorly explained by current genomic deep learning models

bioRxiv (Cold Spring Harbor Laboratory)(2023)10.1101/2023.06.30.547100

N/A

0DataRank · unranked

18 citations

Richard W. Shuai, Parth Baokar, Ryan Chung, Ruchir Rastogi, Pooja Kathail +2 more

Characterizing uncertainty in predictions of genomic sequence-to-activity models

bioRxiv (Cold Spring Harbor Laboratory)(2023)10.1101/2023.12.21.572730

N/A

0DataRank · unranked

4 citations

Ruchir Rastogi, Pooja Kathail, Richard W. Shuai, Nilah M. Ioannidis, Ayesha Bajwa

An all-atom protein generative model

Proceedings of the National Academy of Sciences(2024)10.1073/pnas.2311500121

N/A

0DataRank · unranked

67 citations

Jinho Kim, Lucy Cheng, Gina El Nesr, Minkai Xu, Richard W. Shuai +2 more

Variants in tubule epithelial regulatory elements mediate most heritable differences in human kidney function

Nature Genetics(2024)10.1038/s41588-024-01904-6

N/A

0DataRank · unranked

27 citations

Pooja Kathail, Richard W. Shuai, Ryan Chung, Reinier J. Grona, Sailaja Peddada +14 more

Current genomic deep learning models display decreased performance in cell type-specific accessible regions

Genome biology(2024)10.1186/s13059-024-03335-2

N/A

0DataRank · unranked

17 citations

Richard W. Shuai, Ryan Chung, Chun Jimmie Ye, Gabriel B. Loeb, Nilah M. Ioannidis +1 more

Variants in tubule epithelial regulatory elements mediate most heritable differences in human kidney function

bioRxiv (Cold Spring Harbor Laboratory)(2024)10.1101/2024.06.18.599625

N/A

0DataRank · unranked

5 citations

Pooja Kathail, Richard W. Shuai, Ryan Chung, Reinier J. Grona, Sailaja Peddada +14 more