Laura C. Huang
- Molecular Biology top 10%
- Surgery top 5%
- Endocrinology, Diabetes and Metabolism top 5%
- Radiology, Nuclear Medicine and Imaging top 5%
- Physiology top 10%
- Co-authors
- Joseph LarnerGail GalaskoKang ChengManassés Claudino FontelesGuillermo RomeroCarson LamDaniel L. RubinCaroline Yu
- Topics
- Metabolism, Diabetes, and Cancer (23 papers)Pancreatic function and diabetes (20 papers)Glycogen Storage Diseases and Myoclonus (7 papers)
- Partner nations
- United StatesCanadaBrazil
In The Last Decade
Laura C. Huang
66 papers receiving 2.1k citations
Peers
Comparison fields: 5 of 108
- Molecular Biology 997
- Surgery 550
- Endocrinology, Diabetes and Metabolism 381
- Radiology, Nuclear Medicine and Imaging 307
- Physiology 302
Countries citing papers authored by Laura C. Huang
This map shows the geographic impact of Laura C. Huang'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 Laura C. Huang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Laura C. Huang more than expected).
Fields of papers citing papers by Laura C. Huang
This network shows the impact of papers produced by Laura C. Huang. 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 Laura C. Huang. The network helps show where Laura C. Huang may publish in the future.
Co-authorship network of co-authors of Laura C. Huang
This figure shows the co-authorship network connecting the top 25 collaborators of Laura C. Huang. A scholar is included among the top collaborators of Laura C. Huang 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 Laura C. Huang. Laura C. Huang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 3 | |
| 3 | 3 | |
| 4 | 4 | |
| 5 | 4 | |
| 6 | 12 | |
| 7 | Opening the Black Box: Visualization of Deep Neural Network for Detection of Disease in Retinal Fundus Photographs | 0 |
| 8 | 9 | |
| 9 | 1 | |
| 10 | 16 | |
| 11 | 87 | |
| 12 | 3 | |
| 13 | Long-term Safety of Vitrectomy for Patients with Floaters | 1 |
| 14 | 82 | |
| 15 | Tear Film Lipid Composition of Schirmer Strips from Patients With and Without Dry Eyes | 1 |
| 16 | 8 | |
| 17 | 16 | |
| 18 | 39 | |
| 19 | 39 | |
| 20 | 179 |
About Laura C. Huang
Laura C. Huang is a scholar working on Ophthalmology, Rheumatology and Cell Biology, having authored 70 papers that have together received 2.2k indexed citations. Recurring topics across this work include Metabolism, Diabetes, and Cancer (23 papers), Pancreatic function and diabetes (20 papers) and Glycogen Storage Diseases and Myoclonus (7 papers). The work is most often cited by research in Biochemistry (203 citations), Ophthalmology (247 citations) and Endocrinology, Diabetes and Metabolism (381 citations). Laura C. Huang has collaborated with scholars based in United States, Canada and Brazil. Frequent co-authors include Joseph Larner, Gail Galasko, Kang Cheng, Manassés Claudino Fonteles, Guillermo Romero, Carson Lam, Daniel L. Rubin, Caroline Yu, Charles F. Schwartz and Yunbae Pak. Their work appears in journals such as Science, Proceedings of the National Academy of Sciences and Journal of Biological Chemistry.
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.