Kevin Yang

3.6k total citations · 2 hit papers
64 papers, 1.8k citations indexed

About

Kevin Yang is a scholar working on Molecular Biology, Artificial Intelligence and Surgery. According to data from OpenAlex, Kevin Yang has authored 64 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Molecular Biology, 12 papers in Artificial Intelligence and 6 papers in Surgery. Recurrent topics in Kevin Yang's work include Protein Structure and Dynamics (10 papers), Machine Learning in Bioinformatics (9 papers) and RNA and protein synthesis mechanisms (8 papers). Kevin Yang is often cited by papers focused on Protein Structure and Dynamics (10 papers), Machine Learning in Bioinformatics (9 papers) and RNA and protein synthesis mechanisms (8 papers). Kevin Yang collaborates with scholars based in United States, United Kingdom and China. Kevin Yang's co-authors include Frances H. Arnold, Claire N. Bedbrook, Zachary Wu, Viviana Gradinaru, Chang Hwan Kim, Jiyoung Kim, Austin J. Rice, Alex X. Lu, Peter S. Kim and Brian Hie and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Journal of the American Chemical Society.

In The Last Decade

Kevin Yang

55 papers receiving 1.7k citations

Hit Papers

Learned protein embeddings for machine learning 2018 2026 2020 2023 2018 2024 50 100 150 200

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Kevin Yang United States 23 1.0k 151 143 139 134 64 1.8k
Catherine Mooney Ireland 30 1.8k 1.8× 160 1.1× 105 0.7× 407 2.9× 115 0.9× 115 3.3k
Shuhui Liu China 24 976 1.0× 64 0.4× 43 0.3× 179 1.3× 134 1.0× 104 2.2k
Firas Khatib United States 11 643 0.6× 80 0.5× 48 0.3× 258 1.9× 98 0.7× 16 2.1k
Dániel Schwarz Germany 31 1.7k 1.7× 61 0.4× 146 1.0× 151 1.1× 376 2.8× 121 3.5k
Madhavi K. Ganapathiraju United States 19 641 0.6× 92 0.6× 41 0.3× 92 0.7× 118 0.9× 55 1.2k
Liya Ding China 16 942 0.9× 104 0.7× 72 0.5× 235 1.7× 84 0.6× 76 2.3k
Michael Hucka United States 25 2.6k 2.6× 259 1.7× 85 0.6× 150 1.1× 148 1.1× 57 3.2k
Zhe Feng China 21 690 0.7× 23 0.2× 134 0.9× 247 1.8× 94 0.7× 108 1.8k
Yu Shi United States 17 781 0.8× 178 1.2× 114 0.8× 209 1.5× 60 0.4× 49 1.5k
Anthony Gitter United States 22 1.0k 1.0× 187 1.2× 82 0.6× 106 0.8× 113 0.8× 66 1.7k

Countries citing papers authored by Kevin Yang

Since Specialization
Citations

This map shows the geographic impact of Kevin Yang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kevin Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kevin Yang more than expected).

Fields of papers citing papers by Kevin Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kevin Yang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kevin Yang. The network helps show where Kevin Yang may publish in the future.

Co-authorship network of co-authors of Kevin Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Kevin Yang. A scholar is included among the top collaborators of Kevin Yang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Kevin Yang. Kevin Yang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Alamdari, Sarah, et al.. (2025). ProtNote: a multimodal method for protein–function annotation. Bioinformatics. 41(5). 1 indexed citations
2.
Yang, Huijie, Wenbo Ma, Kevin Yang, Jiali Liao, & Yue Zhao. (2025). Antithrombotic Effect of Ginkgo Biloba Extract: A Study Based on Network Pharmacological Analysis and In vivo Experiments. Nigerian Journal of Clinical Practice. 28(5). 624–632.
3.
Yang, Kevin, Nicolò Fusi, & Alex X. Lu. (2024). Convolutions are competitive with transformers for protein sequence pretraining. Cell Systems. 15(3). 286–294.e2. 43 indexed citations
4.
Johnson, Sean R., et al.. (2024). Computational scoring and experimental evaluation of enzymes generated by neural networks. Nature Biotechnology. 43(3). 396–405. 33 indexed citations
5.
Wu, Kevin, Kevin Yang, Rianne van den Berg, et al.. (2024). Protein structure generation via folding diffusion. Nature Communications. 15(1). 1059–1059. 76 indexed citations breakdown →
6.
Yang, Kevin, et al.. (2023). Virtual Reality and Augmented Reality in Plastic and Craniomaxillofacial Surgery: A Scoping Review. Bioengineering. 10(4). 480–480. 25 indexed citations
7.
Yang, Kevin, et al.. (2023). PREADD: Prefix-Adaptive Decoding for Controlled Text Generation. 10018–10037.
8.
Yang, Kevin, et al.. (2023). Inhibitory Investigations of Acyl-CoA Derivatives against Human Lipoxygenase Isozymes. International Journal of Molecular Sciences. 24(13). 10941–10941. 4 indexed citations
10.
Lv, Gang, Qiong Shi, Ting Zhang, et al.. (2023). Integrating a phenotypic screening with a structural simplification strategy to identify 4-phenoxy-quinoline derivatives to potently disrupt the mitotic localization of Aurora kinase B. Bioorganic & Medicinal Chemistry. 80. 117173–117173. 7 indexed citations
11.
Subramanian, Sanjay, Medhini Narasimhan, Kevin Yang, et al.. (2023). Modular Visual Question Answering via Code Generation. 14 indexed citations
12.
Yang, Kevin, et al.. (2022). Safety reports in dermatology: A 5-year analysis at an academic medical center. Journal of the American Academy of Dermatology. 88(4). 950–952.
13.
Yang, Kevin, Yuandong Tian, Nanyun Peng, & Dan Klein. (2022). Re3: Generating Longer Stories With Recursive Reprompting and Revision. 4393–4479. 45 indexed citations
14.
Wu, Zachary, et al.. (2020). Signal Peptides Generated by Attention-Based Neural Networks. ACS Synthetic Biology. 9(8). 2154–2161. 65 indexed citations
15.
Bedbrook, Claire N., Kevin Yang, J. Elliott Robinson, et al.. (2019). Machine learning-guided channelrhodopsin engineering enables minimally invasive optogenetics. Nature Methods. 16(11). 1176–1184. 136 indexed citations
16.
Yang, Kevin, Zachary Wu, & Frances H. Arnold. (2018). Machine learning in protein engineering. arXiv (Cornell University). 2 indexed citations
17.
Li, Yan, et al.. (2018). Probing the Roles of Physical Forces in Early Chick Embryonic Morphogenesis. Journal of Visualized Experiments. 1 indexed citations
18.
Bedbrook, Claire N., Kevin Yang, Austin J. Rice, Viviana Gradinaru, & Frances H. Arnold. (2017). Machine learning to design integral membrane channelrhodopsins for efficient eukaryotic expression and plasma membrane localization. PLoS Computational Biology. 13(10). e1005786–e1005786. 82 indexed citations
19.
Lorestani, Alexander, Lilach Sheiner, Kevin Yang, et al.. (2010). A Toxoplasma MORN1 Null Mutant Undergoes Repeated Divisions but Is Defective in Basal Assembly, Apicoplast Division and Cytokinesis. PLoS ONE. 5(8). e12302–e12302. 74 indexed citations
20.
Blachon, Stéphanie, et al.. (2009). A Proximal Centriole-Like Structure Is Present in Drosophila Spermatids and Can Serve as a Model to Study Centriole Duplication. Genetics. 182(1). 133–144. 114 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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