Gareth Peat
Impact in
- Genetics top 10%
- Genetic Associations and Epidemiology
- Genomics and Rare Diseases
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- Bioinformatics and Genomic Networks
- RNA modifications and cancer
- Gene expression and cancer classification
- Epigenetics and DNA Methylation
- Single-cell and spatial transcriptomics
Papers in
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- Bioinformatics and Genomic Networks 2
- Epigenetics and DNA Methylation 1
- Gene expression and cancer classification 1
- Protein Degradation and Inhibitors 1
- Genetics 2
- Genetic Associations and Epidemiology 2
- Co-authors
- Ian Dunham (4 shared papers)Alfredo Miranda (2 shared papers)Jeffrey C. Barrett (2 shared papers)Luca Fumis (2 shared papers)Eliseo Papa (2 shared papers)Miguel Carmona (2 shared papers)Miguel Pignatelli (2 shared papers)Michaela Spitzer (1 shared paper)
- Journals
- Nucleic Acids Research (1 paper)Nature Genetics (1 paper)Cancer Research (1 paper)Bioinformatics (1 paper)
- Partner nations
- United KingdomUnited StatesGermany
In The Last Decade
Gareth Peat
4 papers receiving 650 citations
Gareth Peat's Hit Papers
Peers
Comparison fields: 5 of 89
- Genetics 187
- Molecular Biology 341
- Cancer Research 64
- Computational Theory and Mathematics 71
- Immunology 72
Countries citing papers authored by Gareth Peat
This map shows the geographic impact of Gareth Peat'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 Gareth Peat with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gareth Peat more than expected).
Fields of papers citing papers by Gareth Peat
This network shows the impact of papers produced by Gareth Peat. 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 Gareth Peat. The network helps show where Gareth Peat may publish in the future.
Co-authors
The 25 scholars most cited alongside Gareth Peat, 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 | 2018 | 289 | |
| 2 | An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci Hit paper breakdown → | 2021 | 247 |
| 3 | 2017 | 110 | |
| 4 | 2020 | 13 |
About Gareth Peat
Gareth Peat is a scholar working on Molecular Biology, Genetics, Oncology, Computational Theory and Mathematics and Cancer Research, having authored 4 papers that have together received 659 indexed citations. Recurring topics across this work include Genetic Associations and Epidemiology (2 papers), Bioinformatics and Genomic Networks (2 papers), Epigenetics and DNA Methylation (1 paper), Computational Drug Discovery Methods (1 paper), Cancer Genomics and Diagnostics (1 paper), Gene expression and cancer classification (1 paper), Protein Degradation and Inhibitors (1 paper) and Cancer-related Molecular Pathways (1 paper). The work is most often cited by research in Genetics (187 citations), Molecular Biology (341 citations), Cancer Research (64 citations), Computational Theory and Mathematics (71 citations) and Immunology (72 citations). Gareth Peat has collaborated with scholars based in United Kingdom, United States and Germany. Frequent co-authors include Ian Dunham, Alfredo Miranda, Jeffrey C. Barrett, Luca Fumis, Eliseo Papa, Miguel Carmona, Miguel Pignatelli, Michaela Spitzer, Gautier Koscielny and Andrea Pierleoni. Their work appears in journals such as Nucleic Acids Research, Nature Genetics, Cancer Research and Bioinformatics.
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.