Steven Watterson

2.9k citations
28 papers · 842 · h-index 13

Impact in

Papers in

    • Bioinformatics and Genomic Networks 7
    • Gene Regulatory Network Analysis 5
    • Microbial Metabolic Engineering and Bioproduction 3
    • Cholesterol and Lipid Metabolism 4

Steven Watterson

25 papers receiving 828 citations

Peers

Steven Watterson
Comparison fields: 5 of 111
  • Dermatology 97
  • Immunology 176
  • Cancer Research 83
  • Surgery 235
  • Virology 22
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Yue Luo China
You-kyung Choi United States
David W. A. Beno United States
Yawen Wang China
Raj K. Tiwari United States
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Citations per field
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Citations per year

Countries citing papers authored by Steven Watterson

Since Specialization
Citations

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

Fields of papers citing papers by Steven Watterson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2011227
2 2019140
3 2016112
4 201364
5 200838
6 202133
7 202032
8 201527
9 201726
10 201026
11 201225
12 201814
13 202014
14 202112
15 201711
16 201010
17 202210
18 20215
19 20234
20 20173

About Steven Watterson

Steven Watterson is a scholar working on Molecular Biology, Surgery, Computational Theory and Mathematics, Immunology and Cardiology and Cardiovascular Medicine, having authored 28 papers that have together received 842 indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (7 papers), Computational Drug Discovery Methods (5 papers), Gene Regulatory Network Analysis (5 papers), Cholesterol and Lipid Metabolism (4 papers), Microbial Metabolic Engineering and Bioproduction (3 papers), Cardiac Fibrosis and Remodeling (3 papers), ECG Monitoring and Analysis (2 papers) and Lipid metabolism and biosynthesis (2 papers). The work is most often cited by research in Dermatology (97 citations), Immunology (176 citations), Cancer Research (83 citations), Surgery (235 citations) and Virology (22 citations). Steven Watterson has collaborated with scholars based in United Kingdom, Ireland and Singapore. Frequent co-authors include Peter Ghazal, Victoria McGilligan, Anthony J. Bjourson, Maurice O’Kane, Raymond Henderson, Andrew McDowell, Alison Layton, Emma Barnard, Joseph McLaughlin and Kevin A. Robertson. Their work appears in journals such as Clinical and Experimental Rheumatology, Briefings in Bioinformatics, European Heart Journal, BMC Systems Biology and Bioinformatics.

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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