Debswapna Bhattacharya

1.8k total citations
39 papers, 1.2k citations indexed

About

Debswapna Bhattacharya is a scholar working on Molecular Biology, Materials Chemistry and Computational Theory and Mathematics. According to data from OpenAlex, Debswapna Bhattacharya has authored 39 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Molecular Biology, 22 papers in Materials Chemistry and 5 papers in Computational Theory and Mathematics. Recurrent topics in Debswapna Bhattacharya's work include Protein Structure and Dynamics (35 papers), Enzyme Structure and Function (21 papers) and Machine Learning in Bioinformatics (17 papers). Debswapna Bhattacharya is often cited by papers focused on Protein Structure and Dynamics (35 papers), Enzyme Structure and Function (21 papers) and Machine Learning in Bioinformatics (17 papers). Debswapna Bhattacharya collaborates with scholars based in United States and Belarus. Debswapna Bhattacharya's co-authors include Jianlin Cheng, Renzhi Cao, Badri Adhikari, Jie Hou, Jilong Li, Miao Sun, Jesse Eickholt, Trevor A. Norton, T. M. Murali and Xin Deng and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Bioinformatics.

In The Last Decade

Debswapna Bhattacharya

36 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Debswapna Bhattacharya United States 15 996 310 191 108 77 39 1.2k
Gordon Lemmon United States 12 949 1.0× 218 0.7× 165 0.9× 86 0.8× 156 2.0× 16 1.3k
Mateusz Kurciński Poland 16 1.1k 1.1× 219 0.7× 256 1.3× 82 0.8× 186 2.4× 28 1.3k
Gyu Rie Lee South Korea 14 748 0.8× 148 0.5× 122 0.6× 66 0.6× 99 1.3× 23 909
Nicholas M. Glykos Greece 18 926 0.9× 300 1.0× 103 0.5× 66 0.6× 49 0.6× 50 1.2k
Panagiotis I. Koukos Netherlands 13 907 0.9× 123 0.4× 186 1.0× 151 1.4× 107 1.4× 20 1.3k
Robin Pearce United States 20 1.3k 1.3× 326 1.1× 190 1.0× 212 2.0× 133 1.7× 28 1.8k
Alberto J. M. Martín Chile 16 1.2k 1.2× 299 1.0× 129 0.7× 89 0.8× 30 0.4× 54 1.5k
Woong‐Hee Shin South Korea 18 756 0.8× 174 0.6× 455 2.4× 108 1.0× 81 1.1× 43 1.1k
Con Dogovski Australia 20 716 0.7× 272 0.9× 193 1.0× 118 1.1× 51 0.7× 45 1.4k
Jishou Ruan China 23 1.3k 1.3× 159 0.5× 213 1.1× 76 0.7× 85 1.1× 64 1.6k

Countries citing papers authored by Debswapna Bhattacharya

Since Specialization
Citations

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

Fields of papers citing papers by Debswapna Bhattacharya

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Debswapna Bhattacharya

This figure shows the co-authorship network connecting the top 25 collaborators of Debswapna Bhattacharya. A scholar is included among the top collaborators of Debswapna Bhattacharya 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 Debswapna Bhattacharya. Debswapna Bhattacharya 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.
Bhattacharya, Debswapna, et al.. (2025). NEFFy: A Versatile Tool for Computing the Number of Effective Sequences. Bioinformatics. 4 indexed citations
2.
3.
Bhattacharya, Debswapna, et al.. (2024). EquiPNAS: improved protein–nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks. Nucleic Acids Research. 52(5). e27–e27. 31 indexed citations
4.
Norton, Trevor A. & Debswapna Bhattacharya. (2024). Sifting through the noise: A survey of diffusion probabilistic models and their applications to biomolecules. Journal of Molecular Biology. 437(6). 168818–168818. 1 indexed citations
5.
Bhattacharya, Debswapna, et al.. (2024). The landscape of RNA 3D structure modeling with transformer networks. Biology Methods and Protocols. 9(1). bpae047–bpae047. 3 indexed citations
6.
Bhattacharya, Debswapna, et al.. (2023). iQDeep: an integrated web server for protein scoring using multiscale deep learning models. Journal of Molecular Biology. 435(14). 168057–168057.
7.
Bhattacharya, Debswapna, et al.. (2023). PIQLE: protein–protein interface quality estimation by deep graph learning of multimeric interaction geometries. Bioinformatics Advances. 3(1). vbad070–vbad070. 3 indexed citations
8.
Bhattacharya, Debswapna, et al.. (2023). The transformative power of transformers in protein structure prediction. Proceedings of the National Academy of Sciences. 120(32). e2303499120–e2303499120. 14 indexed citations
9.
Bhattacharya, Debswapna, et al.. (2023). Contact-Assisted Threading in Low-Homology Protein Modeling. Methods in molecular biology. 2627. 41–59. 2 indexed citations
10.
Bhattacharya, Debswapna, et al.. (2021). Recent Advances in Protein Homology Detection Propelled by Inter-Residue Interaction Map Threading. Frontiers in Molecular Biosciences. 8. 643752–643752. 13 indexed citations
11.
Bhattacharya, Debswapna, et al.. (2021). Hybridized distance- and contact-based hierarchical structure modeling for folding soluble and membrane proteins. PLoS Computational Biology. 17(2). e1008753–e1008753. 6 indexed citations
12.
Bhattacharya, Debswapna, et al.. (2020). QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks. Bioinformatics. 36(Supplement_1). i285–i291. 24 indexed citations
13.
Bhattacharya, Debswapna, et al.. (2020). SPECS: Integration of side-chain orientation and global distance-based measures for improved evaluation of protein structural models. PLoS ONE. 15(2). e0228245–e0228245. 2 indexed citations
14.
Bhattacharya, Debswapna, et al.. (2019). Contact-assisted Protein Threading. 536–536. 1 indexed citations
15.
Cao, Renzhi, Badri Adhikari, Debswapna Bhattacharya, et al.. (2016). QAcon: single model quality assessment using protein structural and contact information with machine learning techniques. Bioinformatics. 33(4). 586–588. 80 indexed citations
16.
Adhikari, Badri, et al.. (2016). ConEVA: a toolbox for comprehensive assessment of protein contacts. BMC Bioinformatics. 17(1). 517–517. 16 indexed citations
17.
Bhattacharya, Debswapna, et al.. (2016). 3Drefine: an interactive web server for efficient protein structure refinement. Nucleic Acids Research. 44(W1). W406–W409. 328 indexed citations
18.
Bhattacharya, Debswapna & Jianlin Cheng. (2015). De novo protein conformational sampling using a probabilistic graphical model. Scientific Reports. 5(1). 16332–16332. 18 indexed citations
19.
Adhikari, Badri, Debswapna Bhattacharya, Renzhi Cao, & Jianlin Cheng. (2015). CONFOLD: Residue-residue contact-guidedab initioprotein folding. Proteins Structure Function and Bioinformatics. 83(8). 1436–1449. 100 indexed citations
20.
Bhattacharya, Debswapna & Jianlin Cheng. (2013). i3Drefine Software for Protein 3D Structure Refinement and Its Assessment in CASP10. PLoS ONE. 8(7). e69648–e69648. 48 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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