Supreeta Vijayakumar

569 total citations
10 papers, 411 citations indexed

About

Supreeta Vijayakumar is a scholar working on Molecular Biology, Renewable Energy, Sustainability and the Environment and Biomedical Engineering. According to data from OpenAlex, Supreeta Vijayakumar has authored 10 papers receiving a total of 411 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 2 papers in Renewable Energy, Sustainability and the Environment and 2 papers in Biomedical Engineering. Recurrent topics in Supreeta Vijayakumar's work include Microbial Metabolic Engineering and Bioproduction (8 papers), Bioinformatics and Genomic Networks (4 papers) and Gene Regulatory Network Analysis (4 papers). Supreeta Vijayakumar is often cited by papers focused on Microbial Metabolic Engineering and Bioproduction (8 papers), Bioinformatics and Genomic Networks (4 papers) and Gene Regulatory Network Analysis (4 papers). Supreeta Vijayakumar collaborates with scholars based in United Kingdom, Italy and Germany. Supreeta Vijayakumar's 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 and has published in prestigious journals such as Proceedings of the National Academy of Sciences, The Plant Journal and PLoS Computational Biology.

In The Last Decade

Supreeta Vijayakumar

10 papers receiving 408 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Supreeta Vijayakumar United Kingdom	8	346	100	23	18	18	10	411
Madhukar S. Dasika United States	7	586 1.7×	188 1.9×	11 0.5×	31 1.7×	8 0.4×	8	626
Elisabeth Yaneske United Kingdom	5	210 0.6×	47 0.5×	8 0.3×	17 0.9×	9 0.5×	7	317
Natalie Stanford United Kingdom	8	320 0.9×	95 0.9×	44 1.9×	8 0.4×	7 0.4×	13	395
Abdelhalim Larhlimi Germany	9	323 0.9×	98 1.0×	14 0.6×	17 0.9×	7 0.4×	15	408
Christophe Chassagnole France	13	752 2.2×	133 1.3×	6 0.3×	19 1.1×	7 0.4×	37	826
Anne Richelle United States	15	502 1.5×	110 1.1×	14 0.6×	14 0.8×	12 0.7×	34	606
Robert Schuetz Switzerland	4	806 2.3×	242 2.4×	19 0.8×	28 1.6×	11 0.6×	6	853
Iván Domenzain Sweden	10	901 2.6×	319 3.2×	31 1.3×	29 1.6×	41 2.3×	13	986
Mihail Anton Sweden	8	666 1.9×	198 2.0×	12 0.5×	31 1.7×	33 1.8×	10	735
Paulo Maia Portugal	9	559 1.6×	306 3.1×	24 1.0×	12 0.7×	7 0.4×	24	605

Countries citing papers authored by Supreeta Vijayakumar

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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10 of 10 papers shown

Vijayakumar, Supreeta, et al.. (2023). Kinetic modeling identifies targets for engineering improved photosynthetic efficiency in potato (Solanum tuberosum cv. Solara). The Plant Journal. 117(2). 561–572. 7 indexed citations

Vijayakumar, Supreeta, et al.. (2022). A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling. Methods in molecular biology. 2399. 87–122. 5 indexed citations

Vijayakumar, Supreeta & Claudio Angione. (2021). Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacterium Synechococcus sp. PCC 7002. STAR Protocols. 2(4). 100837–100837. 8 indexed citations

Vijayakumar, Supreeta, Pattanathu Rahman, & Claudio Angione. (2020). A Hybrid Flux Balance Analysis and Machine Learning Pipeline Elucidates Metabolic Adaptation in Cyanobacteria. iScience. 23(12). 101818–101818. 45 indexed citations

Vijayakumar, Supreeta, et al.. (2020). A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth. Proceedings of the National Academy of Sciences. 117(31). 18869–18879. 77 indexed citations

Stefano, Alessandro Di, Marialisa Scatà, Supreeta Vijayakumar, et al.. (2019). Social dynamics modeling of chrono-nutrition. PLoS Computational Biology. 15(1). e1006714–e1006714. 14 indexed citations

Zampieri, Guido, Supreeta Vijayakumar, Elisabeth Yaneske, & Claudio Angione. (2019). Machine and deep learning meet genome-scale metabolic modeling. PLoS Computational Biology. 15(7). e1007084–e1007084. 203 indexed citations

Vijayakumar, Supreeta, et al.. (2019). Combining metabolic modelling with machine learning accurately predicts yeast growth rate. Lancaster EPrints (Lancaster University). 1 indexed citations

Vijayakumar, Supreeta, et al.. (2017). Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling. Briefings in Bioinformatics. 19(6). 1218–1235. 35 indexed citations

10.

Vijayakumar, Supreeta, et al.. (2017). Optimization of Multi-Omic Genome-Scale Models: Methodologies, Hands-on Tutorial, and Perspectives. Methods in molecular biology. 1716. 389–408. 16 indexed citations

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.

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