Sayantan Mitra

451 total citations
24 papers, 380 citations indexed

About

Sayantan Mitra is a scholar working on Molecular Biology, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Sayantan Mitra has authored 24 papers receiving a total of 380 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 7 papers in Artificial Intelligence and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Sayantan Mitra's work include Glycosylation and Glycoproteins Research (5 papers), Protein Tyrosine Phosphatases (5 papers) and Advanced Data Storage Technologies (4 papers). Sayantan Mitra is often cited by papers focused on Glycosylation and Glycoproteins Research (5 papers), Protein Tyrosine Phosphatases (5 papers) and Advanced Data Storage Technologies (4 papers). Sayantan Mitra collaborates with scholars based in India, United States and Ireland. Sayantan Mitra's co-authors include Sriparna Saha, Amy Barrios, Mohammed Hasanuzzaman, Roberta Noberini, Ziming Zhang, Elena B. Pasquale, John L. Stebbins, Maurizio Pellecchia, William J. Placzek and Si Wang and has published in prestigious journals such as Proceedings of the National Academy of Sciences, PLoS ONE and Analytical Biochemistry.

In The Last Decade

Sayantan Mitra

24 papers receiving 374 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sayantan Mitra India 12 195 71 66 49 43 24 380
Giuseppe Amodeo Italy 13 355 1.8× 34 0.5× 83 1.3× 29 0.6× 21 0.5× 33 624
Georg Wiese Germany 8 148 0.8× 24 0.3× 135 2.0× 54 1.1× 61 1.4× 9 435
Jianpeng Chen China 13 292 1.5× 28 0.4× 59 0.9× 114 2.3× 46 1.1× 42 641
Zhili Guo China 16 188 1.0× 21 0.3× 298 4.5× 27 0.6× 28 0.7× 42 758
Ian P. Barrett United Kingdom 13 392 2.0× 26 0.4× 59 0.9× 59 1.2× 43 1.0× 23 571
Claire Jean-Quartier Austria 15 315 1.6× 52 0.7× 75 1.1× 37 0.8× 16 0.4× 29 575
Sezen Vatansever United States 7 244 1.3× 24 0.3× 22 0.3× 28 0.6× 26 0.6× 11 449
Marcus Bode Germany 10 393 2.0× 17 0.2× 18 0.3× 51 1.0× 82 1.9× 13 560
Ya‐Hui Lin Taiwan 11 69 0.4× 21 0.3× 67 1.0× 25 0.5× 35 0.8× 33 502
Stefan Naulaerts Belgium 16 378 1.9× 25 0.4× 35 0.5× 100 2.0× 107 2.5× 22 713

Countries citing papers authored by Sayantan Mitra

Since Specialization
Citations

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

Fields of papers citing papers by Sayantan Mitra

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sayantan Mitra

This figure shows the co-authorship network connecting the top 25 collaborators of Sayantan Mitra. A scholar is included among the top collaborators of Sayantan Mitra 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 Sayantan Mitra. Sayantan Mitra 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.
Mitra, Sayantan, et al.. (2024). "Reasoning before Responding": Towards Legal Long-form Question Answering with Interpretability. 4922–4930. 1 indexed citations
2.
Mitra, Sayantan, et al.. (2022). Bug report summarization using multi-view multi-objective optimization framework. Proceedings of the Genetic and Evolutionary Computation Conference. 1245–1253. 3 indexed citations
3.
Mitra, Sayantan, Sriparna Saha, & Mohammed Hasanuzzaman. (2020). A Multi-View Deep Neural Network Model for Chemical-Disease Relation Extraction From Imbalanced Datasets. IEEE Journal of Biomedical and Health Informatics. 24(11). 3315–3325. 17 indexed citations
4.
Mitra, Sayantan, Sriparna Saha, & Mohammed Hasanuzzaman. (2020). Multi-view clustering for multi-omics data using unified embedding. Scientific Reports. 10(1). 13654–13654. 19 indexed citations
5.
Mitra, Sayantan, Mohammed Hasanuzzaman, & Sriparna Saha. (2020). A Unified Multi-view Clustering Algorithm Using Multi-objective Optimization Coupled with Generative Model. ACM Transactions on Knowledge Discovery from Data. 14(1). 1–31. 13 indexed citations
6.
Mitra, Sayantan & Sriparna Saha. (2019). A multiobjective multi-view cluster ensemble technique: Application in patient subclassification. PLoS ONE. 14(5). e0216904–e0216904. 16 indexed citations
7.
Mitra, Sayantan, Mohammed Hasanuzzaman, Sriparna Saha, & Andy Way. (2018). Incorporating Deep Visual Features into Multiobjective based Multi-view Search Results Clustering. International Conference on Computational Linguistics. 3793–3805. 2 indexed citations
8.
Mitra, Sayantan, et al.. (2018). Fusion of stability and multi-objective optimization for solving cancer tissue classification problem. Expert Systems with Applications. 113. 377–396. 9 indexed citations
9.
Saha, Sriparna, Sayantan Mitra, & Stefan Krämer. (2018). Exploring Multiobjective Optimization for Multiview Clustering. ACM Transactions on Knowledge Discovery from Data. 12(4). 1–30. 19 indexed citations
10.
Mitra, Sayantan, et al.. (2017). Thrust++: Extending Thrust Framework for Better Abstraction and Performance. 368–377. 4 indexed citations
11.
Saha, Sriparna, et al.. (2017). A Stack-Based Ensemble Framework for Detecting Cancer MicroRNA Biomarkers. Genomics Proteomics & Bioinformatics. 15(6). 381–388. 14 indexed citations
12.
Sarkar, Santonu & Sayantan Mitra. (2015). A Profile Guided Approach to Optimize Branch Divergence While Transforming Applications for GPUs. 176–185. 5 indexed citations
13.
Wang, Si, Roberta Noberini, John L. Stebbins, et al.. (2012). Targeted Delivery of Paclitaxel to EphA2-Expressing Cancer Cells. Clinical Cancer Research. 19(1). 128–137. 56 indexed citations
14.
Wang, Si, William J. Placzek, John L. Stebbins, et al.. (2012). Novel Targeted System To Deliver Chemotherapeutic Drugs to EphA2-Expressing Cancer Cells. Journal of Medicinal Chemistry. 55(5). 2427–2436. 86 indexed citations
15.
Stanford, Stephanie M., Rekha G. Panchal, Matthew D. Falk, et al.. (2012). High-throughput screen using a single-cell tyrosine phosphatase assay reveals biologically active inhibitors of tyrosine phosphatase CD45. Proceedings of the National Academy of Sciences. 109(35). 13972–13977. 27 indexed citations
16.
Sarkar, Santonu, Sayantan Mitra, & Ashok Srinivasan. (2012). Reuse and Refactoring of GPU Kernels to Design Complex Applications. 99. 134–141. 7 indexed citations
17.
Mitra, Sayantan & Amy Barrios. (2008). Identifying Selective Protein Tyrosine Phosphatase Substrates and Inhibitors from a Fluorogenic, Combinatorial Peptide Library. ChemBioChem. 9(8). 1216–1219. 25 indexed citations
18.
Mitra, Sayantan & Amy Barrios. (2007). A series of peptide-based, fluorogenic probes for protein tyrosine phosphatase activity. Analytical Biochemistry. 370(2). 249–251. 11 indexed citations
19.
20.
Mitra, Sayantan & Amy Barrios. (2005). Highly sensitive peptide-based probes for protein tyrosine phosphatase activity utilizing a fluorogenic mimic of phosphotyrosine. Bioorganic & Medicinal Chemistry Letters. 15(23). 5142–5145. 31 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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