Sushmita Basu

458 total citations
23 papers, 264 citations indexed

About

Sushmita Basu is a scholar working on Molecular Biology, Materials Chemistry and Organic Chemistry. According to data from OpenAlex, Sushmita Basu has authored 23 papers receiving a total of 264 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 6 papers in Materials Chemistry and 2 papers in Organic Chemistry. Recurrent topics in Sushmita Basu's work include RNA and protein synthesis mechanisms (14 papers), Protein Structure and Dynamics (11 papers) and Machine Learning in Bioinformatics (8 papers). Sushmita Basu is often cited by papers focused on RNA and protein synthesis mechanisms (14 papers), Protein Structure and Dynamics (11 papers) and Machine Learning in Bioinformatics (8 papers). Sushmita Basu collaborates with scholars based in United States, India and China. Sushmita Basu's co-authors include Ranjit Prasad Bahadur, Lukasz Kurgan, Daisuke Kihara, D. Bhattacharyya, Manoranjan Ghosh, S. N. Jha, Debjani Karmakar, Sanjeev K. Gupta, S. C. Gadkari and Jian Zhang and has published in prestigious journals such as Nucleic Acids Research, Angewandte Chemie International Edition and Journal of Molecular Biology.

In The Last Decade

Sushmita Basu

19 papers receiving 259 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sushmita Basu United States 9 175 78 29 20 14 23 264
Andrew S. W. Wong Singapore 7 224 1.3× 59 0.8× 31 1.1× 21 1.1× 10 0.7× 11 295
Shih-Hsin Huang Taiwan 9 176 1.0× 88 1.1× 23 0.8× 55 2.8× 40 2.9× 16 321
Debjani Bagchi India 10 214 1.2× 41 0.5× 76 2.6× 103 5.2× 22 1.6× 22 421
Yasunobu Sugimoto Japan 12 211 1.2× 32 0.4× 26 0.9× 39 1.9× 36 2.6× 30 410
Wenjing Mu China 8 208 1.2× 80 1.0× 25 0.9× 46 2.3× 61 4.4× 13 377
James A. Davey Canada 9 259 1.5× 104 1.3× 50 1.7× 26 1.3× 9 0.6× 13 356
Vee Vee Cheong Singapore 8 322 1.8× 30 0.4× 17 0.6× 19 0.9× 25 1.8× 10 366
Roman Renger Germany 4 173 1.0× 34 0.4× 39 1.3× 12 0.6× 3 0.2× 4 285
Kevin T. Halloran United States 7 190 1.1× 31 0.4× 55 1.9× 110 5.5× 6 0.4× 10 338

Countries citing papers authored by Sushmita Basu

Since Specialization
Citations

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

Fields of papers citing papers by Sushmita Basu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sushmita Basu

This figure shows the co-authorship network connecting the top 25 collaborators of Sushmita Basu. A scholar is included among the top collaborators of Sushmita 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 Sushmita Basu. Sushmita 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.
Basu, Sushmita, Yuedong Yang, & Lukasz Kurgan. (2025). Prediction of nucleic acid binding residues in protein sequences: Recent advances and future prospects. Current Opinion in Structural Biology. 94. 103085–103085.
2.
Basu, Sushmita, Arie Laor, Liga Bennetts, et al.. (2025). Health-Related Quality of Life Among Patients Who Have Survived an Episode of Sepsis in the United States: A Systematic Review. Infectious Diseases and Therapy. 14(2). 385–400.
3.
4.
Hu, Gang, et al.. (2024). flDPnn2: Accurate and Fast Predictor of Intrinsic Disorder in Proteins. Journal of Molecular Biology. 436(17). 168605–168605. 9 indexed citations
5.
Zhang, Jian, Sushmita Basu, Fuhao Zhang, & Lukasz Kurgan. (2024). MERIT: Accurate Prediction of Multi Ligand-binding Residues with Hybrid Deep Transformer Network, Evolutionary Couplings and Transfer Learning. Journal of Molecular Biology. 437(15). 168872–168872. 2 indexed citations
6.
Zhao, Bi, Sushmita Basu, & Lukasz Kurgan. (2024). DescribePROT Database of Residue-Level Protein Structure and Function Annotations. Methods in molecular biology. 2867. 169–184.
7.
Basu, Sushmita, Jing Yu, Daisuke Kihara, & Lukasz Kurgan. (2024). Twenty years of advances in prediction of nucleic acid-binding residues in protein sequences. Briefings in Bioinformatics. 26(1). 2 indexed citations
8.
Basu, Sushmita & Lukasz Kurgan. (2024). Taxonomy-specific assessment of intrinsic disorder predictions at residue and region levels in higher eukaryotes, protists, archaea, bacteria and viruses. Computational and Structural Biotechnology Journal. 23. 1968–1977. 2 indexed citations
9.
Basu, Sushmita, Tamás Hegedűs, & Lukasz Kurgan. (2023). CoMemMoRFPred: Sequence-based Prediction of MemMoRFs by Combining Predictors of Intrinsic Disorder, MoRFs and Disordered Lipid-binding Regions. Journal of Molecular Biology. 435(21). 168272–168272. 7 indexed citations
10.
Basu, Sushmita, Bi Zhao, Eshel Faraggi, et al.. (2023). DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options. Nucleic Acids Research. 52(D1). D426–D433. 7 indexed citations
11.
Zhang, Jian, Sushmita Basu, & Lukasz Kurgan. (2023). HybridDBRpred: improved sequence-based prediction of DNA-binding amino acids using annotations from structured complexes and disordered proteins. Nucleic Acids Research. 52(2). e10–e10. 21 indexed citations
12.
Basu, Sushmita, Jörg Gsponer, & Lukasz Kurgan. (2023). DEPICTER2: a comprehensive webserver for intrinsic disorder and disorder function prediction. Nucleic Acids Research. 51(W1). W141–W147. 12 indexed citations
13.
Basu, Sushmita, Daisuke Kihara, & Lukasz Kurgan. (2023). Computational prediction of disordered binding regions. Computational and Structural Biotechnology Journal. 21. 1487–1497. 25 indexed citations
14.
Basu, Sushmita, et al.. (2022). Impaired nuclear transport induced by juvenile ALS causing P525L mutation in NLS domain of FUS: A molecular mechanistic study. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics. 1870(4). 140766–140766. 7 indexed citations
15.
16.
Basu, Sushmita & Ranjit Prasad Bahadur. (2021). Conservation and coevolution determine evolvability of different classes of disordered residues in human intrinsically disordered proteins. Proteins Structure Function and Bioinformatics. 90(3). 632–644. 6 indexed citations
17.
Basu, Sushmita, et al.. (2021). Unusual RNA binding of FUS RRM studied by molecular dynamics simulation and enhanced sampling method. Biophysical Journal. 120(9). 1765–1776. 8 indexed citations
18.
Basu, Sushmita & Ranjit Prasad Bahadur. (2019). Do sequence neighbours of intrinsically disordered regions promote structural flexibility in intrinsically disordered proteins?. Journal of Structural Biology. 209(2). 107428–107428. 6 indexed citations
19.
Basu, Sushmita & Ranjit Prasad Bahadur. (2016). A structural perspective of RNA recognition by intrinsically disordered proteins. Cellular and Molecular Life Sciences. 73(21). 4075–4084. 59 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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