Eric D. Brown

217 papers receiving 20.3k citations

Eric D. Brown's Hit Papers

CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database 2022 · 1.4k citations
1.4k0+9+18Years since publication10002.0k3.0k

Peers

Eric D. Brown
Comparison fields: 5 of 153
  • Applied Microbiology and Biotechnology 959
  • Molecular Medicine 1.6k
  • Microbiology 547
  • Computational Theory and Mathematics 1.1k
  • Genetics 1.6k
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Citations per year

Countries citing papers authored by Eric D. Brown

Since Specialization
Citations

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

Fields of papers citing papers by Eric D. Brown

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Eric D. Brown. 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 Eric D. Brown. The network helps show where Eric D. Brown may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 222 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Antibiotic resistance—the need for global solutions
Hit paper breakdown →
20133517
2
Antibacterial drug discovery in the resistance era
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20161898
3
A Deep Learning Approach to Antibiotic Discovery
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20201683
4
Genomic-sequence comparison of two unrelated isolates of the human gastric pathogen Helicobacter pylori
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19991646
5
CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database
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20221394
6
A Comprehensive, CRISPR-based Functional Analysis of Essential Genes in Bacteria
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2016554
7
Combinations of antibiotics and nonantibiotic drugs enhance antimicrobial efficacy
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2011456
8 2012410
9 2017292
10
Drug repurposing for antimicrobial discovery
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2019287
11 2018238
12 2013219
13 2020201
14 2012188
15 2004180
16 1995173
17 2006168
18 1999165
19 2005160
20 2011156

About Eric D. Brown

Eric D. Brown is a scholar working on Genetics, Molecular Medicine, Pharmacology, Molecular Biology and Infectious Diseases, having authored 222 papers that have together received 20.9k indexed citations. Recurring topics across this work include Bacterial Genetics and Biotechnology (50 papers), Antibiotic Resistance in Bacteria (16 papers), RNA and protein synthesis mechanisms (15 papers), Microbial Natural Products and Biosynthesis (13 papers), Antimicrobial Resistance in Staphylococcus (7 papers), Computational Drug Discovery Methods (4 papers), Bacteriophages and microbial interactions (3 papers) and Surface Modification and Superhydrophobicity (3 papers). The work is most often cited by research in Applied Microbiology and Biotechnology (959 citations), Molecular Medicine (1.6k citations), Microbiology (547 citations), Computational Theory and Mathematics (1.1k citations) and Genetics (1.6k citations). Eric D. Brown has collaborated with scholars based in Canada, United States and Egypt. Frequent co-authors include Gerard D. Wright, Maya A. Farha, Shawn French, Craig R. MacNair, Lindsey A. Carfrae, Amit P. Bhavsar, Michael A. D’Elia, Brian K. Coombes, Christopher T. Walsh and Shannon B. Falconer. Their work appears in journals such as Journal of Bacteriology, ACS Infectious Diseases, Journal of Biological Chemistry, Nature Chemical Biology and Biochemistry.

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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