Mark B. Flegg

567 citations
27 papers · 359 · h-index 11

Impact in

Papers in

Mark B. Flegg

23 papers receiving 347 citations

Peers

Mark B. Flegg
Comparison fields: 5 of 74
  • Modeling and Simulation 104
  • Biophysics 28
  • Molecular Biology 246
  • Statistical and Nonlinear Physics 35
  • Computational Theory and Mathematics 42
Replace Dan T. Gillespie with:
Dan T. Gillespie United States
Stefan Hellander Sweden
Naama Gal Israel
Dietmar Oelz Australia
Satya N.V. Arjunan Japan
Tom Bland United Kingdom
Sophia Maggelakis United States
Kokoro Iwasawa Japan
Hideki Murakawa Japan
Qian-Yuan Tang China
Mark B. Flegg relative to Dan T. Gillespie United States Dan T. Gillespie's profile →
Citations per field
00.5×3.4×
Dan T. Gillespie · 1×
Citations per year

Countries citing papers authored by Mark B. Flegg

Since Specialization
Citations

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

Fields of papers citing papers by Mark B. Flegg

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark B. Flegg. 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 Mark B. Flegg. The network helps show where Mark B. Flegg may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201167
2 201344
3 201232
4 201330
5 201326
6 201525
7 201424
8 201618
9 201517
10 201415
11 201415
12 20088
13 20107
14 20236
15 20216
16 20205
17 20164
18 20192
19 20082
20 20102

About Mark B. Flegg

Mark B. Flegg is a scholar working on Molecular Biology, Modeling and Simulation, Computational Theory and Mathematics, Public Health, Environmental and Occupational Health and Atmospheric Science, having authored 27 papers that have together received 359 indexed citations. Recurring topics across this work include Gene Regulatory Network Analysis (9 papers), Mathematical Biology Tumor Growth (5 papers), Protein Structure and Dynamics (4 papers), Advanced Mathematical Modeling in Engineering (4 papers), Diffusion and Search Dynamics (4 papers), Malaria Research and Control (3 papers), Ultrasound Imaging and Elastography (2 papers) and Mathematical and Theoretical Epidemiology and Ecology Models (2 papers). The work is most often cited by research in Modeling and Simulation (104 citations), Biophysics (28 citations), Molecular Biology (246 citations), Statistical and Nonlinear Physics (35 citations) and Computational Theory and Mathematics (42 citations). Mark B. Flegg has collaborated with scholars based in Australia, United Kingdom and United States. Frequent co-authors include Radek Erban, S. Jonathan Chapman, Benjamin Franz, Sten Rüdiger, Jennifer A. Flegg, Christian A. Yates, Garegin A. Papoian, Stefan Hellander, Helen M. Byrne and D. L. S. McElwain. Their work appears in journals such as Journal of Theoretical Biology, SIAM Journal on Applied Mathematics, The Journal of Chemical Physics, Bulletin of Mathematical Biology and Journal of The Royal Society Interface.

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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