Pinaki Sarder

2.6k total citations
81 papers, 1.3k citations indexed

About

Pinaki Sarder is a scholar working on Artificial Intelligence, Molecular Biology and Biophysics. According to data from OpenAlex, Pinaki Sarder has authored 81 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Artificial Intelligence, 23 papers in Molecular Biology and 19 papers in Biophysics. Recurrent topics in Pinaki Sarder's work include AI in cancer detection (25 papers), Chronic Kidney Disease and Diabetes (13 papers) and Cell Image Analysis Techniques (11 papers). Pinaki Sarder is often cited by papers focused on AI in cancer detection (25 papers), Chronic Kidney Disease and Diabetes (13 papers) and Cell Image Analysis Techniques (11 papers). Pinaki Sarder collaborates with scholars based in United States, South Korea and Portugal. Pinaki Sarder's co-authors include Arye Nehorai, Samuel Achilefu, Brandon Ginley, John E. Tomaszewski, Brendon Lutnick, Kuang‐Yu Jen, Rabi Yacoub, Sanjay Jain, Avi Z. Rosenberg and Ewa K. Stachowiak and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Pinaki Sarder

71 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pinaki Sarder United States 16 327 320 276 258 245 81 1.3k
Shan Tan China 16 414 1.3× 161 0.5× 86 0.3× 338 1.3× 278 1.1× 79 1.4k
Kevin de Haan United States 17 422 1.3× 170 0.5× 465 1.7× 705 2.7× 404 1.6× 37 1.6k
Zhi Lü China 17 217 0.7× 576 1.8× 124 0.4× 218 0.8× 238 1.0× 49 1.4k
Tingying Peng Germany 15 219 0.7× 200 0.6× 501 1.8× 239 0.9× 318 1.3× 44 1.3k
Jonghee Yoon South Korea 24 913 2.8× 215 0.7× 78 0.3× 487 1.9× 243 1.0× 56 2.2k
Ke Si China 20 654 2.0× 93 0.3× 124 0.4× 386 1.5× 146 0.6× 69 1.3k
Yuanming Feng China 18 234 0.7× 136 0.4× 64 0.2× 194 0.8× 85 0.3× 93 864
Geoffrey S. Young United States 23 342 1.0× 485 1.5× 89 0.3× 616 2.4× 187 0.8× 76 2.3k
David Mayerich United States 17 265 0.8× 204 0.6× 74 0.3× 662 2.6× 126 0.5× 80 1.1k
Gregor Urban United States 12 143 0.4× 115 0.4× 381 1.4× 80 0.3× 398 1.6× 24 1.6k

Countries citing papers authored by Pinaki Sarder

Since Specialization
Citations

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

Fields of papers citing papers by Pinaki Sarder

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pinaki Sarder

This figure shows the co-authorship network connecting the top 25 collaborators of Pinaki Sarder. A scholar is included among the top collaborators of Pinaki Sarder 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 Pinaki Sarder. Pinaki Sarder 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.
Shao, M., Jorge Cuadros, Joshua Foreman, et al.. (2025). Machine Learning to Diagnose Complications of Diabetes. Journal of Diabetes Science and Technology. 19(6). 1650–1670.
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Yoshida, Teruhiko, Avi Z. Rosenberg, Komuraiah Myakala, et al.. (2024). PKR activation-induced mitochondrial dysfunction in HIV-transgenic mice with nephropathy. eLife. 12. 1 indexed citations
5.
Shepard, Blythe D., Ryan Kurtz, Avi Z. Rosenberg, et al.. (2024). Nascent shifts in renal cellular metabolism, structure, and function due to chronic empagliflozin in prediabetic mice. American Journal of Physiology-Cell Physiology. 326(4). C1272–C1290. 2 indexed citations
6.
Yoshida, Teruhiko, Komuraiah Myakala, Bryce A. Jones, et al.. (2024). NAD deficiency contributes to progressive kidney disease in HIV-nephropathy mice. American Journal of Physiology-Renal Physiology. 327(3). F450–F462. 1 indexed citations
7.
Calumby, Rodrigo Tripodi, Ângelo Duarte, Emanuele Santos, et al.. (2023). Toward Real-World Computational Nephropathology. Clinical Journal of the American Society of Nephrology. 18(6). 809–812. 2 indexed citations
8.
Ginley, Brandon, Jarcy Zee, Sayat Mimar, et al.. (2023). Correlating Deep Learning-Based Automated Reference Kidney Histomorphometry with Patient Demographics and Creatinine. Kidney360. 4(12). 1726–1737. 8 indexed citations
9.
Yoshida, Teruhiko, Avi Z. Rosenberg, Komuraiah Myakala, et al.. (2023). PKR activation-induced mitochondrial dysfunction in HIV-transgenic mice with nephropathy. eLife. 12. 3 indexed citations
10.
Eadon, Michael T., et al.. (2023). Computational Pathology Fusing Spatial Technologies. Clinical Journal of the American Society of Nephrology. 18(5). 675–677. 1 indexed citations
11.
Zheng, Xiaoyi, Lauren E. Higdon, Alexandre Gaudet, et al.. (2022). Endothelial Cell-Specific Molecule-1 Inhibits Albuminuria in Diabetic Mice. Kidney360. 3(12). 2059–2076. 1 indexed citations
12.
Yoshida, Teruhiko, Clark M. Henderson, Joshua A. Lieberman, et al.. (2022). Variant APOL1 protein in plasma associates with larger particles in humans and mouse models of kidney injury. PLoS ONE. 17(10). e0276649–e0276649. 4 indexed citations
13.
Daneshpajouhnejad, Parnaz, Xiaoping Yang, Xiaoxin X. Wang, et al.. (2022). PodoCount: A Robust, Fully Automated, Whole-Slide Podocyte Quantification Tool. Kidney International Reports. 7(6). 1377–1392. 8 indexed citations
14.
Becker, Jan U., Jeffrey C. Miecznikowski, Avi Z. Rosenberg, et al.. (2021). PodoSighter: A Cloud-Based Tool for Label-Free Podocyte Detection in Kidney Whole-Slide Images. Journal of the American Society of Nephrology. 32(11). 2795–2813. 14 indexed citations
15.
Ginley, Brandon, Brendon Lutnick, Kuang‐Yu Jen, et al.. (2019). Computational Segmentation and Classification of Diabetic Glomerulosclerosis. Journal of the American Society of Nephrology. 30(10). 1953–1967. 123 indexed citations
16.
Sarder, Pinaki, Brendon Lutnick, Rabi Yacoub, et al.. (2018). Deep variational auto-encoders for unsupervised glomerular classification. 1511. 8–8. 1 indexed citations
17.
Narla, Sridhar, Courtney A. Benson, Pinaki Sarder, et al.. (2017). Common developmental genome deprogramming in schizophrenia — Role of Integrative Nuclear FGFR1 Signaling (INFS). Schizophrenia Research. 185. 17–32. 46 indexed citations
18.
Sarder, Pinaki, et al.. (2014). Near infrared fluorescence quenching properties of copper (II) ions for potential applications in biological imaging. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8956. 89560K–89560K. 4 indexed citations
19.
Sarder, Pinaki, et al.. (2013). Dynamic optical projection of acquired luminescence for aiding oncologic surgery. Journal of Biomedical Optics. 18(12). 120501–120501. 12 indexed citations
20.
Sarder, Pinaki, William Schierding, J. Perren Cobb, & Arye Nehorai. (2010). Estimating Sparse Gene Regulatory Networks Using a Bayesian Linear Regression. IEEE Transactions on NanoBioscience. 9(2). 121–131. 7 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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