David Yang

1.7k total citations · 1 hit paper
17 papers, 1.1k citations indexed

About

David Yang is a scholar working on Infectious Diseases, Animal Science and Zoology and Genetics. According to data from OpenAlex, David Yang has authored 17 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Infectious Diseases, 7 papers in Animal Science and Zoology and 6 papers in Genetics. Recurrent topics in David Yang's work include Viral gastroenteritis research and epidemiology (11 papers), Animal Virus Infections Studies (7 papers) and Viral Infections and Immunology Research (4 papers). David Yang is often cited by papers focused on Viral gastroenteritis research and epidemiology (11 papers), Animal Virus Infections Studies (7 papers) and Viral Infections and Immunology Research (4 papers). David Yang collaborates with scholars based in United States, China and Qatar. David Yang's co-authors include Peng Tian, Pardis C. Sabeti, Cheri M. Ackerman, Cameron Myhrvold, Hayden C. Metsky, Paul C. Blainey, Deborah T. Hung, Chloe K. Boehm, Amber Carter and John Barnes and has published in prestigious journals such as Nature, Cell and Nature Communications.

In The Last Decade

David Yang

17 papers receiving 1.1k citations

Hit Papers

Massively multiplexed nucleic acid detection with Cas13 2020 2026 2022 2024 2020 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Yang United States 13 631 424 285 129 121 17 1.1k
Basem Al-Shayeb United States 17 1.4k 2.2× 338 0.8× 192 0.7× 33 0.3× 69 0.6× 19 1.8k
Eun‐Jin Choi South Korea 19 353 0.6× 313 0.7× 65 0.2× 237 1.8× 84 0.7× 89 1.0k
Wilfried A.M. Bakker Netherlands 20 360 0.6× 398 0.9× 143 0.5× 53 0.4× 380 3.1× 49 933
Xiangjun Song China 17 335 0.5× 148 0.3× 66 0.2× 183 1.4× 40 0.3× 74 778
Adi Beth-Din Israel 12 527 0.8× 371 0.9× 53 0.2× 58 0.4× 45 0.4× 36 972
Jianke Wang China 17 184 0.3× 259 0.6× 76 0.3× 268 2.1× 44 0.4× 62 750
Jinhai Huang China 17 328 0.5× 292 0.7× 31 0.1× 267 2.1× 74 0.6× 67 815
Miguel Ángel Sanz Spain 21 520 0.8× 385 0.9× 26 0.1× 116 0.9× 301 2.5× 33 1.0k
Peili Hou China 16 209 0.3× 302 0.7× 57 0.2× 90 0.7× 57 0.5× 35 789
Feng Cong China 16 134 0.2× 371 0.9× 100 0.4× 287 2.2× 65 0.5× 56 718

Countries citing papers authored by David Yang

Since Specialization
Citations

This map shows the geographic impact of David 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 David Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Yang more than expected).

Fields of papers citing papers by David Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David 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 David Yang. The network helps show where David Yang may publish in the future.

Co-authorship network of co-authors of David Yang

This figure shows the co-authorship network connecting the top 25 collaborators of David Yang. A scholar is included among the top collaborators of David 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 David Yang. David Yang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

17 of 17 papers shown
1.
Diaz, Daniel J., Chengyue Gong, J.M. Wells, et al.. (2024). Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations. Nature Communications. 15(1). 6170–6170. 28 indexed citations
2.
Joshi, Saurabh, et al.. (2023). Oracle Agreement: From an Honest Super Majority to Simple Majority. 1 indexed citations
3.
Metsky, Hayden C., Nicole L. Welch, Nicholas J. Haradhvala, et al.. (2022). Designing sensitive viral diagnostics with machine learning. Nature Biotechnology. 40(7). 1123–1131. 51 indexed citations
4.
Xue, James R., Jacob C. Ulirsch, Joe R. Davis, et al.. (2021). Genome-Wide Functional Screen of 3’UTR Variants Uncovers Causal Variants for Human Disease and Evolution. SSRN Electronic Journal. 2 indexed citations
5.
Xue, James R., Steven K. Reilly, Jacob C. Ulirsch, et al.. (2021). Genome-wide functional screen of 3′UTR variants uncovers causal variants for human disease and evolution. Cell. 184(20). 5247–5260.e19. 100 indexed citations
6.
Ackerman, Cheri M., Cameron Myhrvold, Sri Gowtham Thakku, et al.. (2020). Massively multiplexed nucleic acid detection with Cas13. Nature. 582(7811). 277–282. 592 indexed citations breakdown →
7.
Li, Qianqian, Dapeng Wang, David Yang, Shan Lei, & Peng Tian. (2017). Binding of Escherichia coli Does Not Protect Tulane Virus from Heat-Inactivation Regardless the Expression of HBGA-Like Molecules. Frontiers in Microbiology. 8. 1746–1746. 12 indexed citations
8.
Tian, Peng, David Yang, Shan Lei, et al.. (2017). Estimation of Human Norovirus Infectivity from Environmental Water Samples by In Situ Capture RT-qPCR Method. Food and Environmental Virology. 10(1). 29–38. 33 indexed citations
9.
Tian, Peng, David Yang, Shan Lei, et al.. (2017). Concurrent Detection of Human Norovirus and Bacterial Pathogens in Water Samples from an Agricultural Region in Central California Coast. Frontiers in Microbiology. 8. 1560–1560. 23 indexed citations
10.
Lei, Shan, David Yang, Dapeng Wang, & Peng Tian. (2016). Comparison of cell-based and PCR-based assays as methods for measuring infectivity of Tulane virus. Journal of Virological Methods. 231. 1–7. 10 indexed citations
11.
Wang, Dapeng, et al.. (2014). Alternative methods to determine infectivity of Tulane virus: A surrogate for human nororvirus. Food Microbiology. 48. 22–27. 8 indexed citations
12.
Wang, Dapeng, et al.. (2014). New In Situ Capture Quantitative (Real-Time) Reverse Transcription-PCR Method as an Alternative Approach for Determining Inactivation of Tulane Virus. Applied and Environmental Microbiology. 80(7). 2120–2124. 30 indexed citations
13.
Tian, Peng, et al.. (2013). Inactivation of the Tulane Virus, a Novel Surrogate for the Human Norovirus. Journal of Food Protection. 76(4). 712–718. 62 indexed citations
14.
Tian, Peng, David Yang, & Robert E. Mandrell. (2011). A simple method to recover Norovirus from fresh produce with large sample size by using histo-blood group antigen-conjugated to magnetic beads in a recirculating affinity magnetic separation system (RCAMS). International Journal of Food Microbiology. 147(3). 223–227. 17 indexed citations
15.
Tian, Peng, David Yang, & Robert E. Mandrell. (2011). Differences in the Binding of Human Norovirus to and from Romaine Lettuce and Raspberries by Water and Electrolyzed Waters. Journal of Food Protection. 74(8). 1364–1369. 20 indexed citations
16.
Tian, Peng, David Yang, Liangwen Pan, & Robert E. Mandrell. (2011). Application of a Receptor-Binding Capture Quantitative Reverse Transcription-PCR Assay To Concentrate Human Norovirus from Sewage and To Study the Distribution and Stability of the Virus. Applied and Environmental Microbiology. 78(2). 429–436. 26 indexed citations
17.
Tian, Peng, David Yang, Xi Jiang, et al.. (2010). Specificity and kinetics of norovirus binding to magnetic bead-conjugated histo-blood group antigens. Journal of Applied Microbiology. 109(5). no–no. 61 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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