Daniel Poon

17 papers receiving 556 citations

Peers

Daniel Poon
Comparison fields: 5 of 79
  • Health Informatics 251
  • General Dentistry 9
  • Family Practice 10
  • Radiology, Nuclear Medicine and Imaging 72
  • Computer Science Applications 19
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Price United States
Ahmad R. Al‐Qudimat Qatar
Jungwei Fan United States
Jan Clusmann Germany
Scott Askin Switzerland
Evelyne Bischof China
Shreya Gupta United States
Michaela Unger Germany
Meliha Yetişgen United States
Daniel Poon relative to Price United States Price's profile →
Citations per field
00.5×9.4×
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Citations per year

Countries citing papers authored by Daniel Poon

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Poon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Daniel Poon, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel Poon Line = papers co-authored together Daniel Poon links everyone, so they are left out of the graph.

All Works

18 of 18 papers shown
#Work
1 2020329
2 200878
3 201535
4 201521
5
CHIR-265 is a potent selective inhibitor of c-Raf/B-Raf/mutB-Raf that effectively inhibits proliferation and survival of cancer cell lines with Ras/Raf pathway mutations
200620
6 200920
7 201515
8 202013
9 201411
10 20159
11 20178
12 20205
13 20102
14 20202
15 20191
16 20201
17 20201
18 20230

About Daniel Poon

Daniel Poon is a scholar working on Molecular Biology, Organic Chemistry, Oncology, Pulmonary and Respiratory Medicine and Computational Theory and Mathematics, having authored 18 papers that have together received 571 indexed citations. Recurring topics across this work include Melanoma and MAPK Pathways (9 papers), Synthesis and biological activity (5 papers), Computational Drug Discovery Methods (3 papers), Kidney Stones and Urolithiasis Treatments (2 papers), Cancer Immunotherapy and Biomarkers (2 papers), Multiple Myeloma Research and Treatments (2 papers), Management of metastatic bone disease (1 paper) and COVID-19 epidemiological studies (1 paper). The work is most often cited by research in Health Informatics (251 citations), General Dentistry (9 citations), Family Practice (10 citations), Radiology, Nuclear Medicine and Imaging (72 citations) and Computer Science Applications (19 citations). Daniel Poon has collaborated with scholars based in United Kingdom, Switzerland and United States. Frequent co-authors include Rohit Srinivasan, Keerthini Muthuswamy, Ashik Amlani, Cherry Sit, Jonathan Fortman, Robert M. Rodriguez, Valerie Ng, Kristin M. Brinner, Jeffrey T. Bagdanoff and Wooseok Han. Their work appears in journals such as Bioorganic & Medicinal Chemistry Letters, ACS Medicinal Chemistry Letters, Cancer Research, Ophthalmology and Synlett.

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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