Michael J. Elmore

7.7k total citations
18 papers, 644 citations indexed

About

Michael J. Elmore is a scholar working on Infectious Diseases, Molecular Biology and Immunology. According to data from OpenAlex, Michael J. Elmore has authored 18 papers receiving a total of 644 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Infectious Diseases, 8 papers in Molecular Biology and 4 papers in Immunology. Recurrent topics in Michael J. Elmore's work include Tuberculosis Research and Epidemiology (3 papers), Bacteriophages and microbial interactions (3 papers) and Botulinum Toxin and Related Neurological Disorders (3 papers). Michael J. Elmore is often cited by papers focused on Tuberculosis Research and Epidemiology (3 papers), Bacteriophages and microbial interactions (3 papers) and Botulinum Toxin and Related Neurological Disorders (3 papers). Michael J. Elmore collaborates with scholars based in United Kingdom, United States and Singapore. Michael J. Elmore's co-authors include Nigel P. Minton, Tony Atkinson, Triona O’Keeffe, Anne McLeod, Monika Bokori‐Brown, John K. Brehm, Julia A. Tree, Ann Williams, Philip D. Marsh and Richard Vipond and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Applied and Environmental Microbiology.

In The Last Decade

Michael J. Elmore

18 papers receiving 628 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Michael J. Elmore United Kingdom 12 253 251 195 84 83 18 644
Jean‐Christophe Marvaud France 22 603 2.4× 328 1.3× 394 2.0× 63 0.8× 210 2.5× 35 1.1k
Mayuresh M. Abhyankar United States 19 374 1.5× 277 1.1× 70 0.4× 86 1.0× 11 0.1× 37 805
Robert L. Bull United States 11 65 0.3× 269 1.1× 55 0.3× 104 1.2× 17 0.2× 14 395
Dandan Dong China 15 239 0.9× 240 1.0× 39 0.2× 41 0.5× 6 0.1× 27 538
Altaira D. Dearborn United States 16 84 0.3× 322 1.3× 32 0.2× 134 1.6× 9 0.1× 23 539
Louis de Léséleuc Canada 9 159 0.6× 208 0.8× 12 0.1× 64 0.8× 31 0.4× 13 582
Kenji Toyonaga Japan 9 336 1.3× 328 1.3× 27 0.1× 26 0.3× 33 0.4× 17 1.1k
Maren Rautenberg Germany 10 408 1.6× 560 2.2× 12 0.1× 80 1.0× 29 0.3× 12 883
Chiara Vimercati Italy 14 130 0.5× 251 1.0× 14 0.1× 40 0.5× 10 0.1× 25 642
Amanda B. Lasnik United States 11 109 0.4× 194 0.8× 9 0.0× 38 0.5× 38 0.5× 14 545

Countries citing papers authored by Michael J. Elmore

Since Specialization
Citations

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

Fields of papers citing papers by Michael J. Elmore

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael J. Elmore. 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 Michael J. Elmore. The network helps show where Michael J. Elmore may publish in the future.

Co-authorship network of co-authors of Michael J. Elmore

This figure shows the co-authorship network connecting the top 25 collaborators of Michael J. Elmore. A scholar is included among the top collaborators of Michael J. Elmore based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michael J. Elmore. Michael J. Elmore is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
1.
Thursz, Mark, Fouzia Sadiq, Julia A. Tree, et al.. (2023). Inhibition of phosphodiesterase 12 results in antiviral activity against several RNA viruses including SARS-CoV-2. Journal of General Virology. 104(7). 1 indexed citations
2.
Dong, Xiaofeng, Julia A. Tree, Logan Banadyga, et al.. (2023). Linked Mutations in the Ebola Virus Polymerase Are Associated with Organ Specific Phenotypes. Microbiology Spectrum. 11(2). e0415422–e0415422. 2 indexed citations
3.
Tree, Julia A., Jeremy E. Turnbull, Karen R. Buttigieg, et al.. (2020). Unfractionated heparin inhibits live wild type SARS‐CoV‐2 cell infectivity at therapeutically relevant concentrations. British Journal of Pharmacology. 178(3). 626–635. 62 indexed citations
4.
Dong, Xiaofeng, Jordana Muñoz‐Basagoiti, Natasha Y. Rickett, et al.. (2020). Variation around the dominant viral genome sequence contributes to viral load and outcome in patients with Ebola virus disease. Genome biology. 21(1). 238–238. 13 indexed citations
5.
Kidd, Stephen P., Richard Vipond, Michael J. Elmore, et al.. (2018). Development and Assessment of a Diagnostic DNA Oligonucleotide Microarray for Detection and Typing of Meningitis-Associated Bacterial Species. SHILAP Revista de lepidopterología. 7(4). 32–32. 1 indexed citations
6.
Kempsell, Karen E., Stephen P. Kidd, Kuiama Lewandowski, et al.. (2015). Whole genome protein microarrays for serum profiling of immunodominant antigens of Bacillus anthracis. Frontiers in Microbiology. 6. 747–747. 9 indexed citations
7.
Tree, Julia A., Helen C. Flick-Smith, Michael J. Elmore, & Caroline A. Rowland. (2014). The Impact of “Omic” and Imaging Technologies on Assessing the Host Immune Response to Biodefence Agents. Journal of Immunology Research. 2014. 1–17. 6 indexed citations
8.
9.
Tree, Julia A., Jyoti Patel, Ruth Thom, et al.. (2010). Temporal changes in the gene signatures of BCG-vaccinated guinea pigs in response to different mycobacterial antigens. Vaccine. 28(50). 7979–7986. 7 indexed citations
10.
Chaudhuri, Roy R., Gemma Vincent, Nigel Silman, et al.. (2007). Genome Sequencing Shows that European Isolates of Francisella tularensis Subspecies tularensis Are Almost Identical to US Laboratory Strain Schu S4. PLoS ONE. 2(4). e352–e352. 41 indexed citations
11.
Tree, Julia A., et al.. (2006). Development of a Guinea Pig Immune Response-Related Microarray and Its Use To Define the Host Response followingMycobacterium bovisBCG Vaccination. Infection and Immunity. 74(2). 1436–1441. 29 indexed citations
12.
Vaughan, Thomas E., Paul Skipp, Michael J. Hudson, et al.. (2006). Proteomic analysis of Neisseria lactamica and N eisseria meningitidis outer membrane vesicle vaccine antigens. Vaccine. 24(25). 5277–5293. 42 indexed citations
13.
Herbert, Michael, et al.. (2003). Gene transfer intoClostridium difficileCD630 and characterisation of its methylase genes. FEMS Microbiology Letters. 229(1). 103–110. 12 indexed citations
14.
O’Keeffe, Triona, et al.. (2002). Conjugative transfer of clostridial shuttle vectors from Escherichia coli to Clostridium difficile through circumvention of the restriction barrier. Molecular Microbiology. 46(2). 439–452. 204 indexed citations
15.
Elmore, Michael J., et al.. (1995). Nucleotide Sequence of the Gene Coding for Proteolytic (Group I) Clostridium botulinum Type F Neurotoxin: Genealogical Comparison with other Clostridial Neurotoxins. Systematic and Applied Microbiology. 18(1). 23–31. 13 indexed citations
16.
17.
Elmore, Michael J., et al.. (1992). The complete amino acid sequence of the Clostridium botulinum type‐E neurotoxin, derived by nucleotide‐sequence analysis of the encoding gene. European Journal of Biochemistry. 204(2). 657–667. 61 indexed citations
18.
Elmore, Michael J., et al.. (1992). Molecular cloning of the Clostridium botulinum structural gene encoding the type B neurotoxin and determination of its entire nucleotide sequence. Applied and Environmental Microbiology. 58(8). 2345–2354. 101 indexed citations

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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