William W. Lau
- Molecular Biology
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Aerospace Engineering
- Biomedical Engineering
- Co-authors
- Gregory D. HagerSharmishtaa SeshamaniMartijn J. SchuemieKevin Bretonnel CohenJuliane FluckJörg HakenbergRafael TorresMichael Krauthammer
- Topics
- Biomedical Text Mining and Ontologies (3 papers)Robotics and Sensor-Based Localization (3 papers)Single-cell and spatial transcriptomics (2 papers)
- Partner nations
- United StatesUnited KingdomNetherlands
In The Last Decade
William W. Lau
15 papers receiving 444 citations
Peers
Comparison fields: 5 of 93
- Molecular Biology 269
- Artificial Intelligence 167
- Computer Vision and Pattern Recognition 69
- Aerospace Engineering 47
- Biomedical Engineering 33
Countries citing papers authored by William W. Lau
This map shows the geographic impact of William W. Lau'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 William W. Lau with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites William W. Lau more than expected).
Fields of papers citing papers by William W. Lau
This network shows the impact of papers produced by William W. Lau. 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 William W. Lau. The network helps show where William W. Lau may publish in the future.
Co-authorship network of co-authors of William W. Lau
This figure shows the co-authorship network connecting the top 25 collaborators of William W. Lau. A scholar is included among the top collaborators of William W. Lau 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 William W. Lau. William W. Lau is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 34 | |
| 4 | 4 | |
| 5 | 17 | |
| 6 | Teaching Computing in Secondary Schools: A Practical Handbook | 1 |
| 7 | 13 | |
| 8 | 2 | |
| 9 | 241 | |
| 10 | A best-fit model for concept vectors in biomedical research grants. | 1 |
| 11 | 8 | |
| 12 | 42 | |
| 13 | 12 | |
| 14 | 47 | |
| 15 | Diphtheria and tetanus immunity among blood donors in Toronto. | 38 |
About William W. Lau
William W. Lau is a scholar working on Periodontics, Virology and Endocrinology, having authored 15 papers that have together received 462 indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (3 papers), Robotics and Sensor-Based Localization (3 papers) and Single-cell and spatial transcriptomics (2 papers). The work is most often cited by research in Endocrinology (30 citations), Artificial Intelligence (167 citations) and Molecular Biology (269 citations). William W. Lau has collaborated with scholars based in United States, United Kingdom and Netherlands. Frequent co-authors include Gregory D. Hager, Sharmishtaa Seshamani, Martijn J. Schuemie, Kevin Bretonnel Cohen, Juliane Fluck, Jörg Hakenberg, Rafael Torres, Michael Krauthammer, Robert Leaman and Patrick Ruch. Their work appears in journals such as Immunity, Genome biology and Journal of Dental Research.
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.