John Lamb
Impact in
- Molecular Biology top 10%
- Protein Structure and Dynamics
- Heat shock proteins research
- Ubiquitin and proteasome pathways
- Machine Learning in Bioinformatics
- Fungal and yeast genetics research
- RNA Research and Splicing
- Genomics and Chromatin Dynamics
- Cell Biology top 5%
- Microtubule and mitosis dynamics
Papers in
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- Protein Structure and Dynamics 9
- Machine Learning in Bioinformatics 2
- Lipid Membrane Structure and Behavior 1
- Chemical Synthesis and Analysis 1
- Genomics and Chromatin Dynamics 1
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- Microtubule and mitosis dynamics 1
- Co-authors
- Philip Hieter (2 shared papers)Stuart Tugendreich (1 shared paper)William A. Michaud (1 shared paper)Robert S. Sikorski (1 shared paper)Arne Elofsson (6 shared papers)Claudio Bassot (3 shared papers)Nanjiang Shu (2 shared papers)Govindarajan Sudha (2 shared papers)
- Journals
- Bioinformatics (2 papers)Journal of Molecular Biology (2 papers)Trends in Biochemical Sciences (1 paper)Nucleic Acids Research (1 paper)Nature Methods (1 paper)
- Partner nations
- SwedenUnited StatesHungary
In The Last Decade
John Lamb
10 papers receiving 1.3k citations
John Lamb's Hit Papers
Peers
Comparison fields: 5 of 73
- Molecular Biology 930
- Cell Biology 181
- Genetics 71
- Endocrinology 10
- Immunology 38
Countries citing papers authored by John Lamb
This map shows the geographic impact of John Lamb'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 John Lamb with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Lamb more than expected).
Fields of papers citing papers by John Lamb
This network shows the impact of papers produced by John Lamb. 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 John Lamb. The network helps show where John Lamb may publish in the future.
Co-authors
The 18 scholars most cited alongside John Lamb, 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 | 1995 | 539 | |
| 2 | Critical assessment of protein intrinsic disorder prediction Hit paper breakdown → | 2021 | 251 |
| 3 | 1994 | 210 | |
| 4 | 2019 | 183 | |
| 5 | 2017 | 29 | |
| 6 | 2021 | 19 | |
| 7 | 2019 | 12 | |
| 8 | 2022 | 11 | |
| 9 | 2021 | 6 | |
| 10 | 2021 | 4 |
About John Lamb
John Lamb is a scholar working on Molecular Biology, Cell Biology, Oncology, Materials Chemistry and Nuclear Energy and Engineering, having authored 10 papers that have together received 1.3k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (9 papers), Machine Learning in Bioinformatics (2 papers), Lipid Membrane Structure and Behavior (1 paper), Microtubule and mitosis dynamics (1 paper), Chemical Synthesis and Analysis (1 paper), Genomics and Chromatin Dynamics (1 paper), Enzyme Structure and Function (1 paper) and Drug Transport and Resistance Mechanisms (1 paper). The work is most often cited by research in Molecular Biology (930 citations), Cell Biology (181 citations), Genetics (71 citations), Endocrinology (10 citations) and Immunology (38 citations). John Lamb has collaborated with scholars based in Sweden, United States and Hungary. Frequent co-authors include Philip Hieter, Stuart Tugendreich, William A. Michaud, Robert S. Sikorski, Arne Elofsson, Claudio Bassot, Nanjiang Shu, Govindarajan Sudha, Åke Västermark and Konstantinos D. Tsirigos. Their work appears in journals such as Bioinformatics, Journal of Molecular Biology, Trends in Biochemical Sciences, Nucleic Acids Research and Nature Methods.
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.