Gabriel Cuéllar-Partida
- Co-authors
- Miguel E. RenteríaNicholas G. MartinAdrián I. CamposLuis M. García‐MarínTimothy L. BaileyRobert C. McLeayEnda M. ByrneStuart MacGregor
- Topics
- Genetic Associations and Epidemiology (13 papers)Genetic Mapping and Diversity in Plants and Animals (5 papers)Health, Environment, Cognitive Aging (4 papers)
- Partner nations
- AustraliaUnited StatesUnited Kingdom
In The Last Decade
Gabriel Cuéllar-Partida
36 papers receiving 803 citations
Peers
Comparison fields: 5 of 112
- Molecular Biology 229
- Genetics 176
- Physiology 103
- Epidemiology 95
- Psychiatry and Mental health 89
Countries citing papers authored by Gabriel Cuéllar-Partida
This map shows the geographic impact of Gabriel Cuéllar-Partida'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 Gabriel Cuéllar-Partida with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gabriel Cuéllar-Partida more than expected).
Fields of papers citing papers by Gabriel Cuéllar-Partida
This network shows the impact of papers produced by Gabriel Cuéllar-Partida. 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 Gabriel Cuéllar-Partida. The network helps show where Gabriel Cuéllar-Partida may publish in the future.
Co-authorship network of co-authors of Gabriel Cuéllar-Partida
This figure shows the co-authorship network connecting the top 25 collaborators of Gabriel Cuéllar-Partida. A scholar is included among the top collaborators of Gabriel Cuéllar-Partida based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Gabriel Cuéllar-Partida. Gabriel Cuéllar-Partida is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 4 | |
| 3 | 16 | |
| 4 | 4 | |
| 5 | 9 | |
| 6 | 50 | |
| 7 | 82 | |
| 8 | 11 | |
| 9 | 10 | |
| 10 | 12 | |
| 11 | 17 | |
| 12 | 12 | |
| 13 | 18 | |
| 14 | 20 | |
| 15 | 78 | |
| 16 | Educational attainment polygenic risk scores predict surface area of cortical regions important for language and memory | 1 |
| 17 | 3 | |
| 18 | 4 | |
| 19 | 18 | |
| 20 | 31 |
About Gabriel Cuéllar-Partida
Gabriel Cuéllar-Partida is a scholar working on Genetics, Endocrine and Autonomic Systems and Biological Psychiatry, having authored 36 papers that have together received 815 indexed citations. Recurring topics across this work include Genetic Associations and Epidemiology (13 papers), Genetic Mapping and Diversity in Plants and Animals (5 papers) and Health, Environment, Cognitive Aging (4 papers). The work is most often cited by research in Biological Psychiatry (21 citations), Ophthalmology (68 citations) and Sensory Systems (32 citations). Gabriel Cuéllar-Partida has collaborated with scholars based in Australia, United States and United Kingdom. Frequent co-authors include Miguel E. Rentería, Nicholas G. Martin, Adrián I. Campos, Luis M. García‐Marín, Timothy L. Bailey, Robert C. McLeay, Enda M. Byrne, Stuart MacGregor, Pik Fang Kho and Sarah E. Medland. Their work appears in journals such as Nucleic Acids Research, Nature Communications 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.