Sumanta Basu

1.9k total citations
64 papers, 1.1k citations indexed

About

Sumanta Basu is a scholar working on Molecular Biology, Organic Chemistry and Finance. According to data from OpenAlex, Sumanta Basu has authored 64 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Molecular Biology, 17 papers in Organic Chemistry and 9 papers in Finance. Recurrent topics in Sumanta Basu's work include Carbohydrate Chemistry and Synthesis (17 papers), Glycosylation and Glycoproteins Research (11 papers) and Erythrocyte Function and Pathophysiology (9 papers). Sumanta Basu is often cited by papers focused on Carbohydrate Chemistry and Synthesis (17 papers), Glycosylation and Glycoproteins Research (11 papers) and Erythrocyte Function and Pathophysiology (9 papers). Sumanta Basu collaborates with scholars based in India, United States and Germany. Sumanta Basu's co-authors include George Michailidis, James B. Brown, Karl Kumbier, Bin Yu, Abhijit Chakrabarti, Charles R. Evans, Alla Karnovsky, Charles Burant, William L. Duren and Debasis Banerjee and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Statistical Association and Bioinformatics.

In The Last Decade

Sumanta Basu

58 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sumanta Basu India 18 372 176 112 99 80 64 1.1k
Shamim Ahmad Bangladesh 22 829 2.2× 75 0.4× 63 0.6× 51 0.5× 39 0.5× 122 2.0k
Marie‐Dominique Devignes France 22 1.0k 2.7× 56 0.3× 101 0.9× 106 1.1× 92 1.1× 88 2.1k
Lin Tan China 29 1.3k 3.5× 119 0.7× 229 2.0× 130 1.3× 28 0.3× 100 3.0k
Lizhong Liu China 25 1.1k 2.9× 96 0.5× 98 0.9× 146 1.5× 37 0.5× 70 2.0k
Andrzej Jankowski Poland 16 339 0.9× 176 1.0× 40 0.4× 66 0.7× 29 0.4× 96 1.4k
Guilherme P. Telles Brazil 14 997 2.7× 110 0.6× 397 3.5× 88 0.9× 29 0.4× 36 2.3k
Christopher A. Lee United States 18 696 1.9× 306 1.7× 137 1.2× 141 1.4× 168 2.1× 71 3.5k
Huaping Zhang China 22 651 1.8× 150 0.9× 206 1.8× 59 0.6× 31 0.4× 123 2.1k
Athanasia Pavlopoulou Greece 22 946 2.5× 42 0.2× 181 1.6× 150 1.5× 58 0.7× 75 1.9k
Sophia Tsoka United Kingdom 25 1.3k 3.4× 133 0.8× 67 0.6× 53 0.5× 16 0.2× 86 2.4k

Countries citing papers authored by Sumanta Basu

Since Specialization
Citations

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

Fields of papers citing papers by Sumanta Basu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sumanta Basu

This figure shows the co-authorship network connecting the top 25 collaborators of Sumanta Basu. A scholar is included among the top collaborators of Sumanta Basu 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 Sumanta Basu. Sumanta Basu 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.
Dutta, Chiranjit, Налини Равишанкер, & Sumanta Basu. (2024). Modeling multivariate positive‐valued time series using R‐INLA. Applied Stochastic Models in Business and Industry. 40(4). 830–849.
2.
Равишанкер, Налини, et al.. (2024). Hierarchical modeling of irregularly spaced financial returns. Stat. 13(2).
3.
Wells, Martin T., et al.. (2023). Time series transcriptome analysis implicates the circadian clock in the Drosophila melanogaster female’s response to sex peptide. Proceedings of the National Academy of Sciences. 120(5). e2214883120–e2214883120. 6 indexed citations
4.
Saha, Arkajyoti, Sumanta Basu, & Abhirup Datta. (2022). RandomForestsGLS: An R package for Random Forests fordependent data. The Journal of Open Source Software. 7(71). 3780–3780. 6 indexed citations
5.
Schlamp, Florencia, et al.. (2021). Dense time-course gene expression profiling of the Drosophila melanogaster innate immune response. BMC Genomics. 22(1). 304–304. 25 indexed citations
6.
Bayraktar, Erol C., Konnor La, Gökhan Ünlü, et al.. (2020). Metabolic coessentiality mapping identifies C12orf49 as a regulator of SREBP processing and cholesterol metabolism. Nature Metabolism. 2(6). 487–498. 36 indexed citations
7.
Wang, Yu, et al.. (2019). A Debiased MDI Feature Importance Measure for Random Forests. arXiv (Cornell University). 32. 8047–8057. 3 indexed citations
8.
Basu, Sumanta, William W. Fisher, Ann S. Hammonds, et al.. (2018). Exploiting regulatory heterogeneity to systematically identify enhancers with high accuracy. Proceedings of the National Academy of Sciences. 116(3). 900–908. 15 indexed citations
9.
Basu, Sumanta, Karl Kumbier, James B. Brown, & Bin Yu. (2018). Iterative random forests to discover predictive and stable high-order interactions. Proceedings of the National Academy of Sciences. 115(8). 1943–1948. 191 indexed citations
10.
11.
Basu, Sumanta, William L. Duren, Charles R. Evans, et al.. (2017). Sparse network modeling and metscape-based visualization methods for the analysis of large-scale metabolomics data. Bioinformatics. 33(10). 1545–1553. 185 indexed citations
12.
Wilms, Ines, Sumanta Basu, Jacob Bien, & David S. Matteson. (2017). Sparse Identification and Estimation of High-Dimensional Vector AutoRegressive Moving Averages. arXiv (Cornell University). 2 indexed citations
13.
Basu, Sumanta, et al.. (2011). F-cell levels are altered with erythrocyte density in sickle cell disease. Blood Cells Molecules and Diseases. 47(2). 117–119. 4 indexed citations
14.
Bhattacharya, Dipankar, et al.. (2010). Differential regulation of redox proteins and chaperones in HbEβ‐thalassemia erythrocyte proteome. PROTEOMICS - CLINICAL APPLICATIONS. 4(5). 480–488. 36 indexed citations
15.
Datta, Anup, Sumanta Basu, & Nirmolendu Roy. (1999). Chemical and immunochemical studies of the O-antigen from enteropathogenic Escherichia coli O158 lipopolysaccharide. Carbohydrate Research. 322(3-4). 219–227. 23 indexed citations
16.
Shashkov, Alexander S., et al.. (1995). Structure of the O-specific side chain of the Escherichia coli O128 lipopolysaccharide. Carbohydrate Research. 277(2). 283–290. 19 indexed citations
17.
Basu, Sumanta, S Schlecht, Manfred Wagner, & Hubert Mayer. (1994). The sialic acid-containing lipopolysaccharides ofSalmonella djakartaandSalmonella isaszeg(serogroup O: 48): Chemical characterization and reactivity with a sialic acid-binding lectin fromCepaea hortensis. FEMS Immunology & Medical Microbiology. 9(3). 189–197. 5 indexed citations
18.
Basu, Sumanta, et al.. (1993). Primary structure of the ploy saccharide chain of virulent pseudomonas solanacearum biotype II lipopolysaccharide. Carbohydrate Research. 250(2). 335–337. 2 indexed citations
19.
Ahmed, Hafiz, et al.. (1993). Purification, characterisation, and carbohydrate specificity of the lectin of Ficus cunia. Carbohydrate Research. 242. 247–263. 10 indexed citations
20.

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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