Supratim Sengupta

814 total citations
41 papers, 561 citations indexed

About

Supratim Sengupta is a scholar working on Molecular Biology, Statistical and Nonlinear Physics and Genetics. According to data from OpenAlex, Supratim Sengupta has authored 41 papers receiving a total of 561 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 10 papers in Statistical and Nonlinear Physics and 10 papers in Genetics. Recurrent topics in Supratim Sengupta's work include RNA and protein synthesis mechanisms (17 papers), Genomics and Phylogenetic Studies (12 papers) and Evolution and Genetic Dynamics (7 papers). Supratim Sengupta is often cited by papers focused on RNA and protein synthesis mechanisms (17 papers), Genomics and Phylogenetic Studies (12 papers) and Evolution and Genetic Dynamics (7 papers). Supratim Sengupta collaborates with scholars based in India, Canada and Israel. Supratim Sengupta's co-authors include Paul G. Higgs, Xiaoguang Yang, Sumit Mukherjee, Rajarshi Ray, Danny Barash, Sanatan Digal, Payal Singh, Neha Aggarwal, Ajit M. Srivastava and Pradipta Bandyopadhyay and has published in prestigious journals such as Physical Review Letters, Bioinformatics and PLoS ONE.

In The Last Decade

Supratim Sengupta

39 papers receiving 549 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Supratim Sengupta India 12 372 121 66 60 50 41 561
Jun-ichi Wakita Japan 14 202 0.5× 124 1.0× 33 0.5× 11 0.2× 62 1.2× 26 665
Srividya Iyer‐Biswas United States 11 396 1.1× 198 1.6× 70 1.1× 9 0.1× 11 0.2× 17 502
M. Seki Japan 12 128 0.3× 70 0.6× 63 1.0× 63 1.1× 61 1.2× 28 541
Sophie de Buyl Belgium 11 154 0.4× 42 0.3× 140 2.1× 127 2.1× 18 0.4× 23 450
Evgeni V. Nikolaev United States 13 587 1.6× 95 0.8× 130 2.0× 6 0.1× 23 0.5× 28 902
Serena Bradde United States 10 467 1.3× 291 2.4× 67 1.0× 5 0.1× 23 0.5× 13 706
B. Bassetti Italy 12 236 0.6× 107 0.9× 110 1.7× 12 0.2× 8 0.2× 32 603
Alex Lang United States 10 499 1.3× 112 0.9× 61 0.9× 5 0.1× 61 1.2× 14 724
Emmanuel Tannenbaum Israel 11 134 0.4× 183 1.5× 11 0.2× 30 0.5× 81 1.6× 37 321
Severin Schink Germany 9 397 1.1× 203 1.7× 31 0.5× 71 1.2× 26 0.5× 13 584

Countries citing papers authored by Supratim Sengupta

Since Specialization
Citations

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

Fields of papers citing papers by Supratim Sengupta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Supratim Sengupta

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

All Works

20 of 20 papers shown
1.
Sengupta, Supratim, et al.. (2024). Evolution of cooperation in multichannel games on multiplex networks. PLoS Computational Biology. 20(12). e1012678–e1012678. 2 indexed citations
2.
Sengupta, Supratim, et al.. (2022). The Effect of Environment on the Evolution and Proliferation of Protocells of Increasing Complexity. Life. 12(8). 1227–1227. 1 indexed citations
3.
Sengupta, Supratim, et al.. (2020). How strategy environment and wealth shape altruistic behaviour: cooperation rules affecting wealth distribution in dynamic networks. Proceedings of the Royal Society B Biological Sciences. 287(1941). 20202250–20202250. 4 indexed citations
4.
Derr, Julien, et al.. (2020). Emergence of ribozyme and tRNA-like structures from mineral-rich muddy pools on prebiotic earth. Journal of Theoretical Biology. 506. 110446–110446. 9 indexed citations
5.
Mukherjee, Sumit, et al.. (2019). RiboD: a comprehensive database for prokaryotic riboswitches. Bioinformatics. 35(18). 3541–3543. 16 indexed citations
6.
Sengupta, Supratim, et al.. (2018). The evolution of antibiotic production rate in a spatial model of bacterial competition. PLoS ONE. 13(10). e0205202–e0205202. 8 indexed citations
7.
Mukherjee, Sumit, Danny Barash, & Supratim Sengupta. (2017). Comparative genomics and phylogenomic analyses of lysine riboswitch distributions in bacteria. PLoS ONE. 12(9). e0184314–e0184314. 13 indexed citations
8.
Sengupta, Supratim, et al.. (2017). Bribery games on inter-dependent regular networks. Scientific Reports. 7(1). 42735–42735. 7 indexed citations
9.
Chakraborty, Brinta, et al.. (2016). Deciphering a survival strategy during the interspecific competition betweenBacillus cereusMSM-S1 andPseudomonassp. MSM-M1. Royal Society Open Science. 3(11). 160438–160438. 11 indexed citations
10.
Aggarwal, Neha, et al.. (2016). Finite population analysis of the effect of horizontal gene transfer on the origin of an universal and optimal genetic code. Physical Biology. 13(3). 36007–36007. 11 indexed citations
11.
Sengupta, Supratim, et al.. (2015). Bribe and Punishment: An Evolutionary Game-Theoretic Analysis of Bribery. PLoS ONE. 10(7). e0133441–e0133441. 21 indexed citations
12.
Sengupta, Supratim & Paul G. Higgs. (2015). Pathways of Genetic Code Evolution in Ancient and Modern Organisms. Journal of Molecular Evolution. 80(5-6). 229–243. 57 indexed citations
13.
Sengupta, Supratim, et al.. (2015). An Efficient Minimum Free Energy Structure-Based Search Method for Riboswitch Identification Based on Inverse RNA Folding. PLoS ONE. 10(7). e0134262–e0134262. 11 indexed citations
14.
Sengupta, Supratim, et al.. (2014). Two Perspectives on the Origin of the Standard Genetic Code. Origins of Life and Evolution of Biospheres. 44(4). 287–291. 6 indexed citations
15.
Aggarwal, Neha, et al.. (2013). Revisiting the Physico-Chemical Hypothesis of Code Origin: An Analysis Based on Code-Sequence Coevolution in a Finite Population. Origins of Life and Evolution of Biospheres. 43(6). 465–489. 13 indexed citations
16.
Sengupta, Supratim, Julien Derr, Anirban Sain, & Andrew D. Rutenberg. (2012). Stuttering Min oscillations withinE. colibacteria: a stochastic polymerization model. Physical Biology. 9(5). 56003–56003. 7 indexed citations
17.
Sengupta, Supratim, et al.. (2012). Classification of HIV-1 Sequences Using Profile Hidden Markov Models. PLoS ONE. 7(5). e36566–e36566. 4 indexed citations
18.
Singh, Payal, et al.. (2009). Riboswitch Detection Using Profile Hidden Markov Models. BMC Bioinformatics. 10(1). 325–325. 32 indexed citations
19.
Sengupta, Supratim, et al.. (2002). NON-EQUILIBRIUM PHASE TRANSITION DYNAMICS BEYOND THE GAUSSIAN APPROXIMATION. 349–355. 1 indexed citations
20.
Ray, Rajarshi & Supratim Sengupta. (2002). Stochastic production of kink-antikink pairs in the presence of an oscillating background. Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields. 65(6). 4 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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