Daniel Sharpe

1.1k citations
26 papers · 484 · h-index 13

Impact in

    • MicroRNA in disease regulation
    • Cancer-related molecular mechanisms research
    • Acute Myeloid Leukemia Research

Papers in

Daniel Sharpe

25 papers receiving 468 citations

Peers

Daniel Sharpe
Comparison fields: 5 of 107
  • Cancer Research 123
  • Hematology 49
  • Molecular Biology 295
  • Statistical and Nonlinear Physics 42
  • Statistics and Probability 21
Replace Alex Graudenzi with:
Alex Graudenzi Italy
Cong Li China
Jeremy G. Hoskins United States
Michael Morrissey United States
Claudia Kalla Germany
Dean Bottino United States
Jiguang Bao China
Verena Becker Germany
Laurent Pujo-Menjouet France
Daniel Sharpe relative to Alex Graudenzi Italy Alex Graudenzi's profile →
Citations per field
00.5×5.3×
Alex Graudenzi · 1×
Citations per year

Countries citing papers authored by Daniel Sharpe

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Sharpe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Daniel Sharpe, 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 Sharpe Line = papers co-authored together Daniel Sharpe links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015133
2 197361
3 201440
4 201231
5 201329
6 202018
7 201318
8 201317
9 202016
10 202116
11 201915
12 201914
13 202013
14
On Slaty Cleavage
200910
15 20238
16 20217
17 20177
18 20187
19 20125
20 20155

About Daniel Sharpe

Daniel Sharpe is a scholar working on Molecular Biology, Statistical and Nonlinear Physics, Ecology, Atomic and Molecular Physics, and Optics and Cancer Research, having authored 26 papers that have together received 484 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (6 papers), Gene Regulatory Network Analysis (4 papers), Protein Structure and Dynamics (3 papers), Spectroscopy and Quantum Chemical Studies (3 papers), Markov Chains and Monte Carlo Methods (3 papers), Advanced biosensing and bioanalysis techniques (2 papers), Advanced Chemical Physics Studies (2 papers) and TGF-β signaling in diseases (2 papers). The work is most often cited by research in Cancer Research (123 citations), Hematology (49 citations), Molecular Biology (295 citations), Statistical and Nonlinear Physics (42 citations) and Statistics and Probability (21 citations). Daniel Sharpe has collaborated with scholars based in United Kingdom, United States and Canada. Frequent co-authors include David J. Wales, Will Gersch, Thomas D. Swinburne, Fiona Furlong, Luke Gubbins, Madeline Murphy, Terence R.J. Lappin, Karolina Weiner‐Gorzel, Sinéad Lindsay and Amanda McCann. Their work appears in journals such as The Journal of Chemical Physics, Physical review. E, Oncotarget, Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences and Stem Cells.

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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