Supreeta Vijayakumar
- Molecular Biology
- Biomedical Engineering
- Renewable Energy, Sustainability and the Environment
- Computational Theory and Mathematics
- Food Science
- Co-authors
- Claudio AngioneGuido ZampieriElisabeth YaneskePíetro LióPattanathu RahmanMarialisa ScatàAurelio La CorteAlessandro Di Stefano
- Topics
- Microbial Metabolic Engineering and Bioproduction (8 papers)Bioinformatics and Genomic Networks (4 papers)Gene Regulatory Network Analysis (4 papers)
- Partner nations
- United KingdomItalyGermany
In The Last Decade
Supreeta Vijayakumar
10 papers receiving 408 citations
Peers
Comparison fields: 5 of 77
- Molecular Biology 346
- Biomedical Engineering 100
- Renewable Energy, Sustainability and the Environment 23
- Computational Theory and Mathematics 18
- Food Science 18
Countries citing papers authored by Supreeta Vijayakumar
This map shows the geographic impact of Supreeta Vijayakumar'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 Supreeta Vijayakumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Supreeta Vijayakumar more than expected).
Fields of papers citing papers by Supreeta Vijayakumar
This network shows the impact of papers produced by Supreeta Vijayakumar. 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 Supreeta Vijayakumar. The network helps show where Supreeta Vijayakumar may publish in the future.
Co-authorship network of co-authors of Supreeta Vijayakumar
This figure shows the co-authorship network connecting the top 25 collaborators of Supreeta Vijayakumar. A scholar is included among the top collaborators of Supreeta Vijayakumar 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 Supreeta Vijayakumar. Supreeta Vijayakumar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 5 | |
| 3 | 8 | |
| 4 | 45 | |
| 5 | 77 | |
| 6 | 14 | |
| 7 | 203 | |
| 8 | Combining metabolic modelling with machine learning accurately predicts yeast growth rate | 1 |
| 9 | 35 | |
| 10 | 16 |
About Supreeta Vijayakumar
Supreeta Vijayakumar is a scholar working on Molecular Biology, Renewable Energy, Sustainability and the Environment and Cancer Research, having authored 10 papers that have together received 411 indexed citations. Recurring topics across this work include Microbial Metabolic Engineering and Bioproduction (8 papers), Bioinformatics and Genomic Networks (4 papers) and Gene Regulatory Network Analysis (4 papers). The work is most often cited by research in Molecular Biology (346 citations), Biomedical Engineering (100 citations) and Health Informatics (3 citations). Supreeta Vijayakumar has collaborated with scholars based in United Kingdom, Italy and Germany. Frequent co-authors include Claudio Angione, Guido Zampieri, Elisabeth Yaneske, Píetro Lió, Pattanathu Rahman, Marialisa Scatà, Aurelio La Corte, Alessandro Di Stefano, Samuel H. Taylor and Elizabete Carmo‐Silva. Their work appears in journals such as Proceedings of the National Academy of Sciences, The Plant Journal and PLoS Computational Biology.
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.