Tim Hubbard
- Molecular Biology top 0.1%
- Protein Structure and Dynamics 43
- Genomics and Phylogenetic Studies 36
- RNA and protein synthesis mechanisms 34
- Machine Learning in Bioinformatics 24
- Genomics and Chromatin Dynamics 10
- RNA modifications and cancer 10
- Genetics, Bioinformatics, and Biomedical Research 8
- Cancer Research top 0.5%
- Materials Chemistry top 1%
- Enzyme Structure and Function 30
- Genetics top 0.5%
- Computational Theory and Mathematics top 0.5%
- Co-authors
- Alexey G. MurzinC. ChothiaSteven E. BrennerThomas A. DownMichael R. StrattonNazneen RahmanP. Andrew FutrealLachlan Coin
- Journals
- Proteins Structure Function and Bioinformatics (22 papers)Bioinformatics (10 papers)Nucleic Acids Research (6 papers)
- Partner nations
- United KingdomUnited StatesSpain
In The Last Decade
Tim Hubbard
122 papers receiving 17.8k citations
Hit Papers
Peers
Comparison fields: 5 of 194
- Molecular Biology 15.6k
- Cancer Research 2.0k
- Materials Chemistry 3.8k
- Genetics 2.0k
- Computational Theory and Mathematics 892
Countries citing papers authored by Tim Hubbard
This map shows the geographic impact of Tim Hubbard'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 Tim Hubbard with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tim Hubbard more than expected).
Fields of papers citing papers by Tim Hubbard
This network shows the impact of papers produced by Tim Hubbard. 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 Tim Hubbard. The network helps show where Tim Hubbard may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Tim Hubbard, 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 | 2025 | 1 | |
| 2 | 2016 | 68 | |
| 3 | 2013 | 341 | |
| 4 | 2012 | 42 | |
| 5 | 2010 | 34 | |
| 6 | 2010 | 1 | |
| 7 | 2006 | 58 | |
| 8 | 2006 | 240 | |
| 9 | 2002 | 208 | |
| 10 | 2000 | 0 | |
| 11 | RMS/coverage graphs | 1999 | 1 |
| 12 | Population statistics of protein structures | 1997 | 5 |
| 13 | Update on protein structure prediction | 1996 | 1 |
| 14 | Understanding protein structure | 1996 | 3 |
| 15 | 1996 | 76 | |
| 16 | Proceedings Of The Third International Conference On Intelligent Systems For Molecular Biology | 1995 | 64 |
| 17 | Use of beta-strand Interaction Pseudo-Potentials in Protein Structure Prediction and Modeling. | 1994 | 9 |
| 18 | 1993 | 1 | |
| 19 | 1991 | 5 | |
| 20 | 1989 | 14 |
About Tim Hubbard
Tim Hubbard is a scholar working on Molecular Biology, Materials Chemistry and Business and International Management, having authored 123 papers that have together received 18.3k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (43 papers), Genomics and Phylogenetic Studies (36 papers), RNA and protein synthesis mechanisms (34 papers), Enzyme Structure and Function (30 papers), Machine Learning in Bioinformatics (24 papers), Genomics and Chromatin Dynamics (10 papers), RNA modifications and cancer (10 papers) and Genetics, Bioinformatics, and Biomedical Research (8 papers). The work is most often cited by research in Molecular Biology (15.6k citations), Cancer Research (2.0k citations) and Materials Chemistry (3.8k citations). Tim Hubbard has collaborated with scholars based in United Kingdom, United States and Spain. Frequent co-authors include Alexey G. Murzin, C. Chothia, Steven E. Brenner, Thomas A. Down, Michael R. Stratton, Nazneen Rahman, P. Andrew Futreal, Lachlan Coin, Mhairi Marshall and Richard Wooster. Their work appears in journals such as Proteins Structure Function and Bioinformatics, Bioinformatics, Nucleic Acids Research, BMC Bioinformatics and Genome biology.
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.