Phil Bradley

1.7k citations
8 papers · 530 · h-index 6

Impact in

Papers in

    • RNA and protein synthesis mechanisms 4
    • Genomics and Phylogenetic Studies 3
    • Protein Structure and Dynamics 3
    • Machine Learning in Bioinformatics 2
    • Advanced biosensing and bioanalysis techniques 1
    • Bacteriophages and microbial interactions 2

Phil Bradley

8 papers receiving 527 citations

Peers

Phil Bradley
Comparison fields: 5 of 85
  • Structural Biology 10
  • Molecular Biology 429
  • Materials Chemistry 163
  • Ecology 76
  • Microbiology 14
Replace Joana Pereira with:
Joana Pereira Germany
Karen Manalastas-Cantos Germany
Aaron L. Lucius United States
Aneerban Bhattacharya United States
Bipasha Barua United States
Guillaume Postic France
Jinwon Jung South Korea
Charu Chaudhry United States
J.M. Betton France
S. Ohlsson France
Phil Bradley relative to Joana Pereira Germany Joana Pereira's profile →
Citations per field
00.5×10×15×20×24×
Joana Pereira · 1×
Citations per year

Countries citing papers authored by Phil Bradley

Since Specialization
Citations

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

Fields of papers citing papers by Phil Bradley

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 24 scholars most cited alongside Phil Bradley, 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 Phil Bradley Line = papers co-authored together Phil Bradley links everyone, so they are left out of the graph.

All Works

8 of 8 papers shown

About Phil Bradley

Phil Bradley is a scholar working on Molecular Biology, Ecology, Materials Chemistry, Genetics and Immunology, having authored 8 papers that have together received 530 indexed citations. Recurring topics across this work include RNA and protein synthesis mechanisms (4 papers), Genomics and Phylogenetic Studies (3 papers), Protein Structure and Dynamics (3 papers), Machine Learning in Bioinformatics (2 papers), Enzyme Structure and Function (2 papers), Bacteriophages and microbial interactions (2 papers), Advanced biosensing and bioanalysis techniques (1 paper) and Immunodeficiency and Autoimmune Disorders (1 paper). The work is most often cited by research in Structural Biology (10 citations), Molecular Biology (429 citations), Materials Chemistry (163 citations), Ecology (76 citations) and Microbiology (14 citations). Phil Bradley has collaborated with scholars based in United States, Sweden and South Africa. Frequent co-authors include David Baker, Kira M.S. Misura, Chu Wang, Ora Schueler‐Furman, Frank DiMaio, Andrew Leaver‐Fay, Ingemar André, Bonnie Berger, Matthew Menke and Lenore Cowen. Their work appears in journals such as Nucleic Acids Research, Journal of Computational Biology, Proceedings of the National Academy of Sciences, PLoS ONE and Science.

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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