Kaberi Das

3.0k total citations
61 papers, 2.4k citations indexed

About

Kaberi Das is a scholar working on Health, Toxicology and Mutagenesis, Molecular Biology and Environmental Chemistry. According to data from OpenAlex, Kaberi Das has authored 61 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Health, Toxicology and Mutagenesis, 17 papers in Molecular Biology and 17 papers in Environmental Chemistry. Recurrent topics in Kaberi Das's work include Per- and polyfluoroalkyl substances research (17 papers), Effects and risks of endocrine disrupting chemicals (9 papers) and Toxic Organic Pollutants Impact (8 papers). Kaberi Das is often cited by papers focused on Per- and polyfluoroalkyl substances research (17 papers), Effects and risks of endocrine disrupting chemicals (9 papers) and Toxic Organic Pollutants Impact (8 papers). Kaberi Das collaborates with scholars based in United States, India and Singapore. Kaberi Das's co-authors include Christopher Lau, Barbara D. Abbott, Carmen R. Wood, Mitchell B. Rosen, Stanley Barone, J. Christopher Corton, Robert D. Zehr, Mark J. Strynar, Andrew B. Lindstrom and Judith E. Schmid and has published in prestigious journals such as Biochemical and Biophysical Research Communications, Neuroscience and Journal of Pharmacology and Experimental Therapeutics.

In The Last Decade

Kaberi Das

57 papers receiving 2.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kaberi Das United States 24 1.2k 975 483 378 353 61 2.4k
Gennaro Giordano United States 28 195 0.2× 930 1.0× 521 1.1× 163 0.4× 313 0.9× 45 2.4k
Nisha S. Sipes United States 30 220 0.2× 1.7k 1.8× 1.2k 2.5× 104 0.3× 137 0.4× 48 3.8k
Eva B. Brittebo Sweden 33 101 0.1× 435 0.4× 897 1.9× 103 0.3× 262 0.7× 123 3.1k
Yuanhong Chen China 30 180 0.1× 550 0.6× 817 1.7× 77 0.2× 40 0.1× 97 2.8k
Dongping Huang China 21 127 0.1× 297 0.3× 296 0.6× 125 0.3× 112 0.3× 87 1.2k
Sanghamitra Bandyopadhyay India 32 124 0.1× 425 0.4× 868 1.8× 61 0.2× 159 0.5× 83 2.9k
Ellen Fritsche Germany 38 63 0.1× 859 0.9× 1.5k 3.1× 191 0.5× 424 1.2× 126 4.2k
Xiao Sun China 24 96 0.1× 104 0.1× 595 1.2× 44 0.1× 157 0.4× 69 1.5k
Qiong Liu China 36 39 0.0× 404 0.4× 1.2k 2.6× 57 0.2× 201 0.6× 228 4.3k
William P. Watson United Kingdom 29 47 0.0× 407 0.4× 1.3k 2.6× 55 0.1× 625 1.8× 120 2.8k

Countries citing papers authored by Kaberi Das

Since Specialization
Citations

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

Fields of papers citing papers by Kaberi Das

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kaberi Das

This figure shows the co-authorship network connecting the top 25 collaborators of Kaberi Das. A scholar is included among the top collaborators of Kaberi Das 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 Kaberi Das. Kaberi Das 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.
Corton, J. Christopher, Jeffrey S. Gift, Scott S. Auerbach, et al.. (2025). Dose–response modeling of effects in mice after exposure to a polyfluoroalkyl substance (Nafion byproduct 2). Toxicological Sciences. 205(2). 380–400. 2 indexed citations
2.
Dye, Janice A., Michael G. Narotsky, Kaberi Das, et al.. (2025). The effects of cadmium and high fructose diet on metabolic and reproductive health in female CD-1 mice. Food and Chemical Toxicology. 206. 115726–115726. 1 indexed citations
3.
Das, Asit Kumar, et al.. (2024). A Feature Ensemble Framework for Stock Market Forecasting Using Technical Analysis and Aquila Optimizer. IEEE Access. 12. 187899–187918. 1 indexed citations
4.
Das, Kaberi, et al.. (2023). Optimizing CNN‐LSTM hybrid classifier using HCA for biomedical image classification. Expert Systems. 40(5). 17 indexed citations
5.
Mishra, Debahuti, et al.. (2023). Sampling strategies for handling data imbalance problem: An Extensive Review. Journal of Statistics and Management Systems. 26(1). 177–187.
6.
Mishra, Debahuti, et al.. (2022). A COVID-19 X-ray image classification model based on an enhanced convolutional neural network and hill climbing algorithms. Multimedia Tools and Applications. 82(9). 14219–14237. 14 indexed citations
7.
Das, Kaberi, Carmen R. Wood, Anatoly A. Starkov, et al.. (2017). Perfluoroalkyl acids-induced liver steatosis: Effects on genes controlling lipid homeostasis. Toxicology. 378. 37–52. 191 indexed citations
8.
Das, Kaberi, et al.. (2016). HYBRIDIZED UNIVARIATE AND MULTIVARIATE FILTER BASED APPROACHES FOR GENE SELECTION. International Journal of Pharma and Bio Sciences. 4 indexed citations
9.
Das, Kaberi, et al.. (2014). Missing Value Imputation Using Hybrid Higher Order Neural Classifier. Indian Journal of Science and Technology. 7(12). 2007–2014. 6 indexed citations
10.
Wambaugh, John F., R. Woodrow Setzer, Jie Liu, et al.. (2013). Dosimetric Anchoring of In Vivo and In Vitro Studies for Perfluorooctanoate and Perfluorooctanesulfonate. Toxicological Sciences. 136(2). 308–327. 39 indexed citations
11.
Rosen, Mitchell B., Kaberi Das, Carmen R. Wood, et al.. (2013). Evaluation of perfluoroalkyl acid activity using primary mouse and human hepatocytes. Toxicology. 308. 129–137. 33 indexed citations
12.
13.
Wambaugh, John F., Kaberi Das, Robert D. Zehr, et al.. (2011). Comparative pharmacokinetics of perfluorononanoic acid in rat and mouse. Toxicology. 281(1-3). 48–55. 65 indexed citations
14.
Butenhoff, John L., James A. Bjork, Shu‐Ching Chang, et al.. (2011). Toxicological evaluation of ammonium perfluorobutyrate in rats: Twenty-eight-day and ninety-day oral gavage studies. Reproductive Toxicology. 33(4). 513–530. 56 indexed citations
15.
Das, Pratap Chandra, et al.. (2009). Biogenic Amines and Ascorbic Acid in Rat Brain Following Steroid Contraceptive Treatment. Experimental and Clinical Endocrinology & Diabetes. 97(1). 103–106. 2 indexed citations
16.
Abbott, Barbara D., Cynthia J. Wolf, Kaberi Das, et al.. (2008). Developmental toxicity of perfluorooctane sulfonate (PFOS) is not dependent on expression of peroxisome proliferator activated receptor-alpha (PPARα) in the mouse. Reproductive Toxicology. 27(3-4). 258–265. 98 indexed citations
17.
18.
Tandon, Pushpa, Yili Yang, Kaberi Das, Gregory L. Holmes, & Carl E. Stafstrom. (1999). Neuroprotective effects of brain-derived neurotrophic factor in seizures during development. Neuroscience. 91(1). 293–303. 64 indexed citations
19.
Das, Kaberi, Jau‐Shyong Hong, & Virginia M. Sanders. (1997). Ultralow concentrations of proenkephalin and [met5]-enkephalin differentially affect IgM and IgG production by B cells. Journal of Neuroimmunology. 73(1-2). 37–46. 9 indexed citations
20.
Das, Kaberi, Michael McMillian, Guoying Bing, & Jau‐Shyong Hong. (1995). Modulatory effects of [Met5]-enkephalin on interleukin-1β secretion from microglia in mixed brain cell cultures. Journal of Neuroimmunology. 62(1). 9–17. 41 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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