Julian Gough
- Molecular Biology top 0.5%
- Genomics and Phylogenetic Studies 32
- RNA and protein synthesis mechanisms 19
- Bioinformatics and Genomic Networks 18
- Protein Structure and Dynamics 13
- Machine Learning in Bioinformatics 8
- Biomedical Text Mining and Ontologies 6
- Genetics top 1%
- Genomics and Rare Diseases 5
- Cell Biology top 1%
- Cancer Research top 5%
- Biotechnology top 2%
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- Enzyme Structure and Function 7
Julian Gough
72 papers receiving 9.3k citations
Hit Papers
Peers
Comparison fields: 5 of 166
- Molecular Biology 7.3k
- Genetics 1.5k
- Cell Biology 832
- Cancer Research 549
- Biotechnology 250
Countries citing papers authored by Julian Gough
This map shows the geographic impact of Julian Gough'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 Julian Gough with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Julian Gough more than expected).
Fields of papers citing papers by Julian Gough
This network shows the impact of papers produced by Julian Gough. 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 Julian Gough. The network helps show where Julian Gough may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Julian Gough, 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 | 2024 | 3 | |
| 2 | 2016 | 46 | |
| 3 | 2016 | 27 | |
| 4 | The genome of Aiptasia , a sea anemone model for coral symbiosisbreakdown → | 2015 | 289 |
| 5 | Classification of Intrinsically Disordered Regions and Proteins | 2014 | 1 |
| 6 | 2014 | 141 | |
| 7 | 2014 | 67 | |
| 8 | 2013 | 9 | |
| 9 | 2013 | 36 | |
| 10 | 2013 | 29 | |
| 11 | 2013 | 37 | |
| 12 | D2P2: database of disordered protein predictionsbreakdown → | 2012 | 524 |
| 13 | Predicting the Functional, Molecular, and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Modelsbreakdown → | 2012 | 899 |
| 14 | 2004 | 32 | |
| 15 | 2003 | 27 | |
| 16 | 2003 | 88 | |
| 17 | SUPERFAMILY:HMMs representing all proteins of known structure | 2002 | 66 |
| 18 | 2002 | 17 | |
| 19 | Assignment of homology to genome sequences using a library of hidden Markov models that represent all proteins of known structurebreakdown → | 2001 | 962 |
| 20 | HPLC Identification of Toxic Non-Protein Amino Acids in the Tropical Leguminous Tree Acacia angustissima | 2000 | 1 |
About Julian Gough
Julian Gough is a scholar working on Molecular Biology, Genetics and Biotechnology, having authored 73 papers that have together received 9.4k indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (32 papers), RNA and protein synthesis mechanisms (19 papers), Bioinformatics and Genomic Networks (18 papers), Protein Structure and Dynamics (13 papers), Machine Learning in Bioinformatics (8 papers), Enzyme Structure and Function (7 papers), Biomedical Text Mining and Ontologies (6 papers) and Genomics and Rare Diseases (5 papers). The work is most often cited by research in Molecular Biology (7.3k citations), Genetics (1.5k citations) and Cell Biology (832 citations). Julian Gough has collaborated with scholars based in United Kingdom, United States and Japan. Frequent co-authors include Cyrus Chothia, Sarah A. Teichmann, Kevin Karplus, Richard Hughey, D.N. Cooper, Ian N.M. Day, Hashem A. Shihab, Tom R. Gaunt, Hai Fang and Christine Vogel. Their work appears in journals such as Science, Chemical Reviews and Proceedings of the National Academy of Sciences.
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.