Shubhra Sankar Ray

731 total citations
43 papers, 451 citations indexed

About

Shubhra Sankar Ray is a scholar working on Molecular Biology, Artificial Intelligence and Cancer Research. According to data from OpenAlex, Shubhra Sankar Ray has authored 43 papers receiving a total of 451 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Molecular Biology, 13 papers in Artificial Intelligence and 9 papers in Cancer Research. Recurrent topics in Shubhra Sankar Ray's work include Gene expression and cancer classification (11 papers), Machine Learning in Bioinformatics (10 papers) and Bioinformatics and Genomic Networks (8 papers). Shubhra Sankar Ray is often cited by papers focused on Gene expression and cancer classification (11 papers), Machine Learning in Bioinformatics (10 papers) and Bioinformatics and Genomic Networks (8 papers). Shubhra Sankar Ray collaborates with scholars based in India, Brazil and United Kingdom. Shubhra Sankar Ray's co-authors include Sankar K. Pal, Sanghamitra Bandyopadhyay, Sanghamitra Bandyopadhyay, Sampa Misra, Aloke Kumar Datta, Dhananjay Bhattacharyya, Sanghamitra Bandyopadhyay, Jagdish C. Bhatia, Ronaldo F. Hashimoto and Fabrício Martins Lopes and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Gene and IEEE Transactions on Biomedical Engineering.

In The Last Decade

Shubhra Sankar Ray

41 papers receiving 432 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shubhra Sankar Ray India 12 202 184 106 49 42 43 451
Patryk Orzechowski United States 7 117 0.6× 275 1.5× 31 0.3× 24 0.5× 33 0.8× 32 416
Pablo Duboue United States 10 333 1.6× 512 2.8× 29 0.3× 80 1.6× 46 1.1× 25 726
Bei Yang China 13 136 0.7× 155 0.8× 89 0.8× 39 0.8× 48 1.1× 35 494
Tapas Bhadra India 10 150 0.7× 139 0.8× 36 0.3× 19 0.4× 80 1.9× 21 339
Xuequn Shang China 10 225 1.1× 130 0.7× 29 0.3× 38 0.8× 28 0.7× 51 398
Rasmita Dash India 12 108 0.5× 251 1.4× 25 0.2× 43 0.9× 73 1.7× 45 430
Raúl Giráldez Spain 8 188 0.9× 140 0.8× 32 0.3× 43 0.9× 24 0.6× 15 325
Di He China 11 147 0.7× 265 1.4× 50 0.5× 14 0.3× 137 3.3× 26 522
Marco Frasca Italy 11 186 0.9× 102 0.6× 55 0.5× 18 0.4× 42 1.0× 31 369

Countries citing papers authored by Shubhra Sankar Ray

Since Specialization
Citations

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

Fields of papers citing papers by Shubhra Sankar Ray

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shubhra Sankar Ray. 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 Shubhra Sankar Ray. The network helps show where Shubhra Sankar Ray may publish in the future.

Co-authorship network of co-authors of Shubhra Sankar Ray

This figure shows the co-authorship network connecting the top 25 collaborators of Shubhra Sankar Ray. A scholar is included among the top collaborators of Shubhra Sankar Ray 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 Shubhra Sankar Ray. Shubhra Sankar Ray 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.
Singh, Joginder & Shubhra Sankar Ray. (2025). Integrating fuzzy rough set-based entropies for identifying drug-resistant miRNAs in cancer. Journal of Computational Science. 91. 102673–102673.
2.
Goyal, Ashima & Shubhra Sankar Ray. (2025). Reducing supply shock-led inflation in emerging markets. Economic Analysis and Policy. 86. 2278–2301.
3.
Singh, Joginder, et al.. (2024). Identifying pan-cancer and cancer subtype miRNAs using interpretable convolutional neural network. Journal of Computational Science. 85. 102510–102510. 1 indexed citations
4.
Singh, Joginder, et al.. (2024). Weighted Combination of Łukasiewicz implication and Fuzzy Jaccard similarity in Hybrid Ensemble Framework (WCLFJHEF) for Gene Selection. Computers in Biology and Medicine. 170. 107981–107981. 1 indexed citations
5.
Kundu, Amrita, et al.. (2022). Predicting drug-resistant miRNAs in cancer. Network Modeling Analysis in Health Informatics and Bioinformatics. 12(1). 2 indexed citations
6.
Misra, Sampa & Shubhra Sankar Ray. (2017). Finding optimum width of discretization for gene expressions using functional annotations. Computers in Biology and Medicine. 90. 59–67. 6 indexed citations
7.
Pal, Sankar K., et al.. (2017). Granular Neural Networks, Pattern Recognition and Bioinformatics. Studies in computational intelligence. 8 indexed citations
8.
Ray, Shubhra Sankar, et al.. (2016). Fuzzy-Rough Entropy Measure and Histogram Based Patient Selection for miRNA Ranking in Cancer. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 15(2). 659–672. 6 indexed citations
9.
Ray, Shubhra Sankar, et al.. (2015). Identifying relevant group of miRNAs in cancer using fuzzy mutual information. Medical & Biological Engineering & Computing. 54(4). 701–710. 13 indexed citations
10.
Lopes, Fabrício Martins, Shubhra Sankar Ray, Ronaldo F. Hashimoto, & Roberto M. César. (2014). Entropic Biological Score: a cell cycle investigation for GRNs inference. Gene. 541(2). 129–137. 11 indexed citations
11.
Ray, Shubhra Sankar, et al.. (2013). Computational Approaches for Identifying Cancer miRNA Expressions. Gene Expression. 15(5). 243–253. 9 indexed citations
12.
Ray, Shubhra Sankar, et al.. (2013). Fuzzy rough sets, and a granular neural network for unsupervised feature selection. Neural Networks. 48. 91–108. 36 indexed citations
13.
Ray, Shubhra Sankar, et al.. (2012). Fuzzy rough granular self-organizing map and fuzzy rough entropy. Theoretical Computer Science. 466. 37–63. 17 indexed citations
14.
Ray, Shubhra Sankar, et al.. (2012). HD-RNAS: An Automated Hierarchical Database of RNA Structures. Frontiers in Genetics. 3. 59–59. 20 indexed citations
15.
Ray, Shubhra Sankar, et al.. (2012). A Weighted Power Framework for Integrating Multisource Information: Gene Function Prediction in Yeast. IEEE Transactions on Biomedical Engineering. 59(4). 1162–1168. 7 indexed citations
16.
Ray, Shubhra Sankar, et al.. (2010). Notice of Retraction: RNA secondary structure prediction in soft computing framework: A review. 220. 430–435. 2 indexed citations
17.
Ray, Shubhra Sankar, Sanghamitra Bandyopadhyay, & Sankar K. Pal. (2007). Gene ordering in partitive clustering using microarray expressions. Journal of Biosciences. 32(S1). 1019–1025. 10 indexed citations
18.
Ray, Shubhra Sankar, et al.. (1992). Mahalanobis distance-based two new feature evaluation criteria. Information Sciences. 60(3). 217–245. 4 indexed citations
19.
Ray, Shubhra Sankar. (1989). On a theoretical property of the bhattacharyya coefficient as a feature evaluation criterion. Pattern Recognition Letters. 9(5). 315–319. 8 indexed citations
20.
Datta, Asoke Kumar, et al.. (1982). Maximum Likelihood Methods in Vowel Recognition: A Comparative Study. IEEE Transactions on Pattern Analysis and Machine Intelligence. PAMI-4(6). 683–689. 6 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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