Jaime G. Carbonell
- Molecular Biology
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Computational Theory and Mathematics top 5%
- Information Systems top 10%
- Co-authors
- Ryszard S. MichalskiJudith Klein‐SeetharamanÖznur TaştanSivaraman BalakrishnanYanjun QiSu‐In LeeChristopher J. LangmeadHetunandan Kamisetty
- Topics
- Machine Learning in Bioinformatics (10 papers)Protein Structure and Dynamics (7 papers)Bioinformatics and Genomic Networks (4 papers)
- Journals
- SHILAP Revista de lepidopterologíaBioinformaticsFrontiers in Immunology
- Partner nations
- United StatesAustriaHong Kong
In The Last Decade
Jaime G. Carbonell
27 papers receiving 1.4k citations
Hit Papers
Peers
Comparison fields: 5 of 174
- Molecular Biology 580
- Artificial Intelligence 383
- Computer Vision and Pattern Recognition 163
- Computational Theory and Mathematics 139
- Information Systems 95
Countries citing papers authored by Jaime G. Carbonell
This map shows the geographic impact of Jaime G. Carbonell'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 Jaime G. Carbonell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jaime G. Carbonell more than expected).
Fields of papers citing papers by Jaime G. Carbonell
This network shows the impact of papers produced by Jaime G. Carbonell. 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 Jaime G. Carbonell. The network helps show where Jaime G. Carbonell may publish in the future.
Co-authorship network of co-authors of Jaime G. Carbonell
This figure shows the co-authorship network connecting the top 25 collaborators of Jaime G. Carbonell. A scholar is included among the top collaborators of Jaime G. Carbonell 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 Jaime G. Carbonell. Jaime G. Carbonell is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 20 | |
| 2 | 10 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 3 | |
| 6 | 4 | |
| 7 | 1 | |
| 8 | 6 | |
| 9 | Machine Learning: An Artificial Intelligence Approachbreakdown → | 497 |
| 10 | 222 | |
| 11 | 16 | |
| 12 | 18 | |
| 13 | 20 | |
| 14 | 74 | |
| 15 | 21 | |
| 16 | 35 | |
| 17 | 2 | |
| 18 | 1 | |
| 19 | 76 | |
| 20 | 19 |
About Jaime G. Carbonell
Jaime G. Carbonell is a scholar working on Transplantation, Experimental and Cognitive Psychology and Virology, having authored 28 papers that have together received 1.5k indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (10 papers), Protein Structure and Dynamics (7 papers) and Bioinformatics and Genomic Networks (4 papers). The work is most often cited by research in Artificial Intelligence (383 citations), Transplantation (31 citations) and Computational Theory and Mathematics (139 citations). Jaime G. Carbonell has collaborated with scholars based in United States, Austria and Hong Kong. Frequent co-authors include Ryszard S. Michalski, Judith Klein‐Seetharaman, Öznur Taştan, Sivaraman Balakrishnan, Yanjun Qi, Su‐In Lee, Christopher J. Langmead, Hetunandan Kamisetty, Weike Pan and James T. Kwok. Their work appears in journals such as SHILAP Revista de lepidopterología, Bioinformatics and Frontiers in Immunology.
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.