Thomas Todd
Impact in
- Endocrinology top 10%
- Escherichia coli research studies
- Small Animals top 10%
- Brucella: diagnosis, epidemiology, treatment
Papers in
-
- vaccines and immunoinformatics approaches 4
- Biomedical Text Mining and Ontologies 3
- Bioinformatics and Genomic Networks 1
- Machine Learning in Bioinformatics 1
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- Tuberculosis Research and Epidemiology 2
- Co-authors
- Yongqun He (5 shared papers)Mario di Bernardo (1 shared paper)Nigel J. Savery (1 shared paper)Krasimira Tsaneva‐Atanasova (1 shared paper)Antoni Matyjaszkiewicz (1 shared paper)Claire Grierson (1 shared paper)Zuoshuang Xiang (4 shared papers)Thomas E. Gorochowski (1 shared paper)
- Journals
- Nucleic Acids Research (2 papers)Lab Animal (1 paper)Molecular Microbiology (1 paper)BMC Bioinformatics (1 paper)PLoS ONE (1 paper)
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Thomas Todd
8 papers receiving 271 citations
Peers
Comparison fields: 5 of 61
- Endocrinology 43
- Small Animals 27
- Infectious Diseases 45
- Molecular Medicine 12
- Molecular Biology 166
Countries citing papers authored by Thomas Todd
This map shows the geographic impact of Thomas Todd'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 Thomas Todd with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Todd more than expected).
Fields of papers citing papers by Thomas Todd
This network shows the impact of papers produced by Thomas Todd. 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 Thomas Todd. The network helps show where Thomas Todd may publish in the future.
Co-authors
The 25 scholars most cited alongside Thomas Todd, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2012 | 78 | |
| 2 | 2013 | 61 | |
| 3 | 2007 | 60 | |
| 4 | 2014 | 40 | |
| 5 | 2013 | 24 | |
| 6 | 2013 | 6 | |
| 7 | Ontology representation and ANOVA analysis of vaccine protection investigation | 2010 | 6 |
| 8 | Vaxar: A Web-Based Database of Laboratory Animal Responses to Vaccinations and Its Application in the Meta-Analysis of Different Animal Responses to Tuberculosis Vaccinations. | 2016 | 4 |
About Thomas Todd
Thomas Todd is a scholar working on Molecular Biology, Infectious Diseases, Small Animals, Computer Networks and Communications and Pharmaceutical Science, having authored 8 papers that have together received 279 indexed citations. Recurring topics across this work include vaccines and immunoinformatics approaches (4 papers), Biomedical Text Mining and Ontologies (3 papers), Tuberculosis Research and Epidemiology (2 papers), Nonlinear Dynamics and Pattern Formation (1 paper), Bioinformatics and Genomic Networks (1 paper), Chemical Reactions and Isotopes (1 paper), Machine Learning in Bioinformatics (1 paper) and Escherichia coli research studies (1 paper). The work is most often cited by research in Endocrinology (43 citations), Small Animals (27 citations), Infectious Diseases (45 citations), Molecular Medicine (12 citations) and Molecular Biology (166 citations). Thomas Todd has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Yongqun He, Mario di Bernardo, Nigel J. Savery, Krasimira Tsaneva‐Atanasova, Antoni Matyjaszkiewicz, Claire Grierson, Zuoshuang Xiang, Thomas E. Gorochowski, Stephen A. Reid and George W. Jourdian. Their work appears in journals such as Nucleic Acids Research, Lab Animal, Molecular Microbiology, BMC Bioinformatics and PLoS ONE.
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.