Tom Smith
Impact in
- Cancer Research top 5%
- Cancer Genomics and Diagnostics
- Cancer-related molecular mechanisms research
- Molecular Biology top 5%
- RNA Research and Splicing
- RNA modifications and cancer
- RNA and protein synthesis mechanisms
- Single-cell and spatial transcriptomics
- Genomics and Phylogenetic Studies
- CRISPR and Genetic Engineering
Papers in ⓘ
-
- RNA Research and Splicing 5
- RNA and protein synthesis mechanisms 5
- RNA modifications and cancer 5
- Genomics and Phylogenetic Studies 3
- Single-cell and spatial transcriptomics 2
- Co-authors
- Ian Sudbery (2 shared papers)Andreas Heger (2 shared papers)Kathryn S. Lilley (9 shared papers)Rayner M. L. Queiroz (5 shared papers)Anne E. Willis (5 shared papers)Veronica Dezi (4 shared papers)Robert F. Harvey (4 shared papers)Mariavittoria Pizzinga (4 shared papers)
- Journals
- Data Science Journal (2 papers)Genome biology (2 papers)Nature Methods (1 paper)Nature Communications (1 paper)Science Advances (1 paper)
- Partner nations
- United KingdomUnited StatesGermany
In The Last Decade
Tom Smith
28 papers receiving 2.0k citations
Hit Papers
Peers
Comparison fields: 5 of 127
- Cancer Research 352
- Molecular Biology 1.5k
- Immunology 184
- Biophysics 44
- Cell Biology 113
Countries citing papers authored by Tom Smith
This map shows the geographic impact of Tom Smith'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 Tom Smith with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tom Smith more than expected).
Fields of papers citing papers by Tom Smith
This network shows the impact of papers produced by Tom Smith. 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 Tom Smith. The network helps show where Tom Smith may publish in the future.
Co-authors
The 25 scholars most cited alongside Tom Smith, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy Hit paper breakdown → | 2017 | 1166 |
| 2 | 2019 | 236 | |
| 3 | 2019 | 140 | |
| 4 | 2019 | 133 | |
| 5 | 2018 | 74 | |
| 6 | 2022 | 48 | |
| 7 | 2016 | 37 | |
| 8 | 2023 | 29 | |
| 9 | 2020 | 22 | |
| 10 | 2018 | 18 | |
| 11 | 2020 | 17 | |
| 12 | 2020 | 16 | |
| 13 | 2003 | 8 | |
| 14 | 2007 | 6 | |
| 15 | 2005 | 5 | |
| 16 | 2004 | 4 | |
| 17 | 2007 | 4 | |
| 18 | 1978 | 3 | |
| 19 | 2020 | 2 | |
| 20 | 2006 | 2 |
About Tom Smith
Tom Smith is a scholar working on Molecular Biology, General Health Professions, Spectroscopy, Cancer Research and Surgery, having authored 30 papers that have together received 2.0k indexed citations. Recurring topics across this work include RNA Research and Splicing (5 papers), RNA and protein synthesis mechanisms (5 papers), RNA modifications and cancer (5 papers), Genomics and Phylogenetic Studies (3 papers), Advanced Proteomics Techniques and Applications (3 papers), Single-cell and spatial transcriptomics (2 papers), Hydrocarbon exploration and reservoir analysis (2 papers) and Healthcare Policy and Management (2 papers). The work is most often cited by research in Cancer Research (352 citations), Molecular Biology (1.5k citations), Immunology (184 citations), Biophysics (44 citations) and Cell Biology (113 citations). Tom Smith has collaborated with scholars based in United Kingdom, United States and Germany. Frequent co-authors include Ian Sudbery, Andreas Heger, Kathryn S. Lilley, Rayner M. L. Queiroz, Anne E. Willis, Veronica Dezi, Robert F. Harvey, Mariavittoria Pizzinga, Laraib Malik and Avi Srivastava. Their work appears in journals such as Data Science Journal, Genome biology, Nature Methods, Nature Communications and Science Advances.
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.