T. A. Webster

1.2k citations
11 papers · 1.0k · h-index 9

Impact in

  • Genetics top 5%
    • Bacterial Genetics and Biotechnology
    • Virus-based gene therapy research
    • RNA and protein synthesis mechanisms
    • Gene expression and cancer classification
    • Genomics and Phylogenetic Studies

Papers in

    • RNA and protein synthesis mechanisms 5
    • Machine Learning in Bioinformatics 2
    • Protein Structure and Dynamics 2
    • Gene expression and cancer classification 1
    • Bacterial Genetics and Biotechnology 2

T. A. Webster

10 papers receiving 1.0k citations

Peers

T. A. Webster
Comparison fields: 5 of 87
  • Genetics 348
  • Molecular Biology 650
  • Oncology 173
  • Aging 8
  • Immunology 67
Replace Barbara E. Pearson with:
Barbara E. Pearson United States
Karel H. M. van Wely Spain
Burkhard Kröger Germany
Paolo Amati Italy
I Bikel United States
Shinako Takada Japan
V.B. Reddy United States
Elizabeth A. Peters United States
Ken Simpson Australia
G H Enders United States
T. A. Webster relative to Barbara E. Pearson United States Barbara E. Pearson's profile →
Citations per field
00.5×
Barbara E. Pearson · 1×
Citations per year

Countries citing papers authored by T. A. Webster

Since Specialization
Citations

This map shows the geographic impact of T. A. Webster'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 T. A. Webster with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T. A. Webster more than expected).

Fields of papers citing papers by T. A. Webster

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by T. A. Webster. 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 T. A. Webster. The network helps show where T. A. Webster may publish in the future.

Co-authors

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

All Works

11 of 11 papers shown
#Work
1 2002374
2 1988195
3 2005135
4 1988117
5 198797
6 198365
7 198231
8 198710
9 19898
10 20155
11 20020

About T. A. Webster

T. A. Webster is a scholar working on Molecular Biology, Genetics, Plant Science, Materials Chemistry and Ecology, having authored 11 papers that have together received 1.0k indexed citations. Recurring topics across this work include RNA and protein synthesis mechanisms (5 papers), Enzyme Structure and Function (2 papers), Machine Learning in Bioinformatics (2 papers), Protein Structure and Dynamics (2 papers), Bacterial Genetics and Biotechnology (2 papers), Folate and B Vitamins Research (1 paper), Powdery Mildew Fungal Diseases (1 paper) and Gene expression and cancer classification (1 paper). The work is most often cited by research in Genetics (348 citations), Molecular Biology (650 citations), Oncology (173 citations), Aging (8 citations) and Immunology (67 citations). T. A. Webster has collaborated with scholars based in United States. Frequent co-authors include Temple F. Smith, Earl Hubbell, Rui Mei, David T. Chin, Tracey A. Smith, Stephen A. Goff, Alfred L. Goldberg, Thomas B. Ryder, Sanne P. Smeekens and Christina A. Harrington. Their work appears in journals such as Journal of Biological Chemistry, Computer applications in the biosciences, Molecular Biology and Evolution, Journal of Virology and Archives of Biochemistry and Biophysics.

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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