Kumar Selvarajoo

1.6k total citations
67 papers, 1.1k citations indexed

About

Kumar Selvarajoo is a scholar working on Molecular Biology, Genetics and Immunology. According to data from OpenAlex, Kumar Selvarajoo has authored 67 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 51 papers in Molecular Biology, 9 papers in Genetics and 8 papers in Immunology. Recurrent topics in Kumar Selvarajoo's work include Gene Regulatory Network Analysis (34 papers), Bioinformatics and Genomic Networks (14 papers) and Single-cell and spatial transcriptomics (10 papers). Kumar Selvarajoo is often cited by papers focused on Gene Regulatory Network Analysis (34 papers), Bioinformatics and Genomic Networks (14 papers) and Single-cell and spatial transcriptomics (10 papers). Kumar Selvarajoo collaborates with scholars based in Singapore, Japan and Canada. Kumar Selvarajoo's co-authors include Vincent Piras, Masaru Tomita, Masa Tsuchiya, Mohamed Helmy, Sangdun Choi, Gwang Lee, Jayalakshmi Krishnan, Derek J. Smith, Alessandro Giuliani and Kentaro Hayashi and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Kumar Selvarajoo

66 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kumar Selvarajoo Singapore	20	665	263	110	108	66	67	1.1k
Huamei Zhang China	16	611 0.9×	176 0.7×	66 0.6×	88 0.8×	57 0.9×	46	1.2k
Vicente Arnau Spain	12	564 0.8×	147 0.6×	82 0.7×	104 1.0×	61 0.9×	37	1.0k
Lingli Zhou China	23	838 1.3×	341 1.3×	120 1.1×	121 1.1×	83 1.3×	58	1.5k
Xinqi Liu China	14	500 0.8×	91 0.3×	93 0.8×	84 0.8×	43 0.7×	42	970
Thomas Stoeger United States	16	1.0k 1.6×	100 0.4×	74 0.7×	133 1.2×	55 0.8×	27	1.4k
Debashis Ghosh United States	17	825 1.2×	156 0.6×	122 1.1×	339 3.1×	90 1.4×	47	1.4k
Cheng Shen China	18	328 0.5×	131 0.5×	152 1.4×	92 0.9×	55 0.8×	63	989
Carlos Alberto Vanegas Prieto Spain	20	865 1.3×	102 0.4×	116 1.1×	106 1.0×	66 1.0×	48	1.4k
Andrew F. Jarnuczak United Kingdom	10	888 1.3×	84 0.3×	82 0.7×	79 0.7×	94 1.4×	15	1.2k

Countries citing papers authored by Kumar Selvarajoo

Since Specialization

Citations

This map shows the geographic impact of Kumar Selvarajoo'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 Kumar Selvarajoo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kumar Selvarajoo more than expected).

Fields of papers citing papers by Kumar Selvarajoo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kumar Selvarajoo. 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 Kumar Selvarajoo. The network helps show where Kumar Selvarajoo may publish in the future.

Co-authorship network of co-authors of Kumar Selvarajoo

This figure shows the co-authorship network connecting the top 25 collaborators of Kumar Selvarajoo. A scholar is included among the top collaborators of Kumar Selvarajoo 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 Kumar Selvarajoo. Kumar Selvarajoo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Tong, Yen Wah, et al.. (2024). Overcoming challenges in microalgal bioprocessing through data-driven and computational approaches. Current Opinion in Food Science. 61. 101253–101253. 3 indexed citations

Yeo, Hock Chuan, et al.. (2024). Identifying effective evolutionary strategies-based protocol for uncovering reaction kinetic parameters under the effect of measurement noises. BMC Biology. 22(1). 235–235. 1 indexed citations

Pabis, Kamil, Diogo Barardo, Jan Gruber, et al.. (2024). The impact of short-lived controls on the interpretation of lifespan experiments and progress in geroscience – Through the lens of the “900-day rule”. Ageing Research Reviews. 101. 102512–102512. 7 indexed citations

Chen, Xixian, et al.. (2024). Systems biology approach for enhancing limonene yield by re-engineering Escherichia coli. npj Systems Biology and Applications. 10(1). 109–109. 3 indexed citations

Helmy, Mohamed, et al.. (2023). Globally invariant behavior of oncogenes and random genes at population but not at single cell level. npj Systems Biology and Applications. 9(1). 28–28. 3 indexed citations

Pabis, Kamil, et al.. (2023). A concerted increase in readthrough and intron retention drives transposon expression during aging and senescence. eLife. 12. 13 indexed citations

Selvarajoo, Kumar, et al.. (2023). Identifying Key In Silico Knockout for Enhancement of Limonene Yield Through Dynamic Metabolic Modelling. Methods in molecular biology. 2745. 3–19. 1 indexed citations

Yeo, Hock Chuan, et al.. (2022). Metabolomics and modelling approaches for systems metabolic engineering. Metabolic Engineering Communications. 15. e00209–e00209. 24 indexed citations

Selvarajoo, Kumar, et al.. (2019). Searching for simple rules in Pseudomonas aeruginosa biofilm formation. BMC Research Notes. 12(1). 763–763. 3 indexed citations

10.

Selvarajoo, Kumar. (2017). Complexity of Biochemical and Genetic Responses Reduced Using Simple Theoretical Models. Methods in molecular biology. 1702. 171–201. 10 indexed citations

11.

Selvarajoo, Kumar. (2017). A systems biology approach to overcome TRAIL resistance in cancer treatment. Progress in Biophysics and Molecular Biology. 128. 142–154. 21 indexed citations

12.

Hayashi, Kentaro, Sho Tabata, Vincent Piras, Masaru Tomita, & Kumar Selvarajoo. (2015). Systems Biology Strategy Reveals PKCÎ´ is Key for Sensitizing TRAIL-Resistant Human Fibrosarcoma. Frontiers in Immunology. 5. 659–659. 15 indexed citations

13.

Piras, Vincent & Kumar Selvarajoo. (2014). The reduction of gene expression variability from single cells to populations follows simple statistical laws. Genomics. 105(3). 137–144. 25 indexed citations

14.

Selvarajoo, Kumar. (2014). Parameter-less approaches for interpreting dynamic cellular response. Journal of Biological Engineering. 8(1). 23–23. 9 indexed citations

15.

Selvarajoo, Kumar & Alessandro Giuliani. (2012). Finding Self-organization from the Dynamic Gene Expressions of Innate Immune Responses. Frontiers in Physiology. 3. 192–192. 8 indexed citations

16.

Tsuchiya, Masa, Vincent Piras, Alessandro Giuliani, Masaru Tomita, & Kumar Selvarajoo. (2010). Collective Dynamics of Specific Gene Ensembles Crucial for Neutrophil Differentiation: The Existence of Genome Vehicles Revealed. PLoS ONE. 5(8). e12116–e12116. 19 indexed citations

17.

Helmy, Mohamed, Jin Gohda, Jun‐ichiro Inoue, et al.. (2009). Predicting Novel Features of Toll-Like Receptor 3 Signaling in Macrophages. PLoS ONE. 4(3). e4661–e4661. 21 indexed citations

18.

Selvarajoo, Kumar, Yasunari Takada, Jin Gohda, et al.. (2008). Signaling Flux Redistribution at Toll-Like Receptor Pathway Junctions. PLoS ONE. 3(10). e3430–e3430. 38 indexed citations

19.

Krishnan, Jayalakshmi, Kumar Selvarajoo, Masa Tsuchiya, Gwang Lee, & Sangdun Choi. (2007). Toll-like receptor signal transduction. Experimental & Molecular Medicine. 39(4). 421–438. 188 indexed citations

20.

Selvarajoo, Kumar. (2006). Discovering differential activation machinery of the Toll‐like receptor 4 signaling pathways in MyD88 knockouts. FEBS Letters. 580(5). 1457–1464. 24 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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