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
- Epigenetics and DNA Methylation
- Gene expression and cancer classification
Papers in ⓘ
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- Bioinformatics and Genomic Networks 3
- Epigenetics and DNA Methylation 1
- Protein Degradation and Inhibitors 1
- Gene expression and cancer classification 1
- Genetics 2
- Genetic Associations and Epidemiology 2
- Co-authors
- Ian Dunham (4 shared papers)Miguel Carmona (2 shared papers)Eliseo Papa (2 shared papers)Jeffrey C. Barrett (2 shared papers)Alfredo Miranda (2 shared papers)Luca Fumis (2 shared papers)Miguel Pignatelli (2 shared papers)Denise Carvalho‐Silva (1 shared paper)
- Journals
- Nature Genetics (1 paper)Nucleic Acids Research (1 paper)Cancer Research (1 paper)Bioinformatics (1 paper)
- Partner nations
- United KingdomUnited States
In The Last Decade
Gareth Peat
4 papers receiving 618 citations
Hit Papers
Peers
Comparison fields: 5 of 90
- Genetics 196
- Molecular Biology 395
- Cancer Research 78
- Computational Theory and Mathematics 80
- Immunology 88
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 | Open Targets Platform: new developments and updates two years on Hit paper breakdown → | 2018 | 284 |
| 2 | An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci Hit paper breakdown → | 2021 | 223 |
| 3 | 2017 | 107 | |
| 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 627 indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (3 papers), Genetic Associations and Epidemiology (2 papers), Epigenetics and DNA Methylation (1 paper), Cancer Genomics and Diagnostics (1 paper), Computational Drug Discovery Methods (1 paper), Protein Degradation and Inhibitors (1 paper), Cancer-related Molecular Pathways (1 paper) and Gene expression and cancer classification (1 paper). The work is most often cited by research in Genetics (196 citations), Molecular Biology (395 citations), Cancer Research (78 citations), Computational Theory and Mathematics (80 citations) and Immunology (88 citations). Gareth Peat has collaborated with scholars based in United Kingdom and United States. Frequent co-authors include Ian Dunham, Miguel Carmona, Eliseo Papa, Jeffrey C. Barrett, Alfredo Miranda, Luca Fumis, Miguel Pignatelli, Denise Carvalho‐Silva, Andrea Pierleoni and Nikiforos Karamanis. Their work appears in journals such as Nature Genetics, Nucleic Acids Research, 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.