Vivek Das

657 total citations
19 papers, 352 citations indexed

About

Vivek Das is a scholar working on Molecular Biology, Endocrinology, Diabetes and Metabolism and Genetics. According to data from OpenAlex, Vivek Das has authored 19 papers receiving a total of 352 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 4 papers in Endocrinology, Diabetes and Metabolism and 3 papers in Genetics. Recurrent topics in Vivek Das's work include Single-cell and spatial transcriptomics (7 papers), Gene expression and cancer classification (5 papers) and Diabetes Treatment and Management (3 papers). Vivek Das is often cited by papers focused on Single-cell and spatial transcriptomics (7 papers), Gene expression and cancer classification (5 papers) and Diabetes Treatment and Management (3 papers). Vivek Das collaborates with scholars based in United States, Denmark and India. Vivek Das's co-authors include Biswapriya B. Misra, S. Sahu, Michał Krassowski, Rajat K. De, Saptarsi Goswami, Pasquale Laise, Giuseppe Testa, Pierre‐Luc Germain, Maria Rosa Abenavoli and Alessandro Vitriolo and has published in prestigious journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Vivek Das

15 papers receiving 344 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Vivek Das United States 7 238 46 36 32 22 19 352
Antoine Bodein Canada 6 310 1.3× 64 1.4× 45 1.3× 16 0.5× 11 0.5× 14 481
Timofey Ivanisenko Russia 13 325 1.4× 31 0.7× 51 1.4× 69 2.2× 23 1.0× 45 501
Jessica Xin Hu Denmark 4 218 0.9× 51 1.1× 62 1.7× 18 0.6× 15 0.7× 5 330
Xinyuan Liu China 8 339 1.4× 61 1.3× 35 1.0× 32 1.0× 78 3.5× 13 503
Kahn Rhrissorrakrai United States 11 226 0.9× 49 1.1× 66 1.8× 17 0.5× 33 1.5× 28 428
Furong Tang China 13 232 1.0× 59 1.3× 63 1.8× 15 0.5× 25 1.1× 35 406
Francisco Salavert Spain 12 322 1.4× 72 1.6× 106 2.9× 17 0.5× 21 1.0× 14 409
Matteo Bersanelli Italy 8 337 1.4× 42 0.9× 64 1.8× 8 0.3× 22 1.0× 11 445
Yupeng Cun China 10 190 0.8× 53 1.2× 27 0.8× 24 0.8× 12 0.5× 19 302
Michał Krassowski United Kingdom 4 220 0.9× 34 0.7× 45 1.3× 10 0.3× 11 0.5× 5 293

Countries citing papers authored by Vivek Das

Since Specialization
Citations

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

Fields of papers citing papers by Vivek Das

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vivek Das

This figure shows the co-authorship network connecting the top 25 collaborators of Vivek Das. A scholar is included among the top collaborators of Vivek 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 Vivek Das. Vivek Das is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Das, Vivek, Otto Bergman, Djordje Djordjevic, et al.. (2026). Multi-omics data integration from patients with carotid stenosis illuminates key molecular signatures of atherosclerotic instability. Genome Medicine. 18(1).
2.
Pruijm, Menno, Petter Bjornstad, David Z.I. Cherney, et al.. (2025). REMODELing mechanistic trials for kidney disease: a multimodal, tissue-centered approach to understand the renal mechanism of action of semaglutide. Kidney International. 109(1). 6–16.
3.
Tuttle, Katherine R., Petter Bjornstad, Menno Pruijm, et al.. (2025). Mechanistic Effects of Semaglutide on Kidney Disease in Type 2 Diabetes: The REMODEL Trial. Journal of the American Society of Nephrology. 36(10S).
4.
Alakwaa, Fadhl, Vivek Das, Årindam Majumdar, et al.. (2025). Leveraging complementary multi-omics data integration methods for mechanistic insights in kidney diseases. JCI Insight. 10(5). 3 indexed citations
5.
Olesen, Emma T. B., et al.. (2024). Single-Cell Advances in Investigating and Understanding Chronic Kidney Disease and Diabetic Kidney Disease. American Journal Of Pathology. 195(1). 55–68. 2 indexed citations
6.
Gluud, Lise Lotte, et al.. (2024). Identification of ligand and receptor interactions in CKD and MASH through the integration of single cell and spatial transcriptomics. PLoS ONE. 19(5). e0302853–e0302853. 3 indexed citations
7.
Pyke, Charles, et al.. (2024). A systematic evaluation of state-of-the-art deconvolution methods in spatial transcriptomics: insights from cardiovascular disease and chronic kidney disease. SHILAP Revista de lepidopterología. 4. 1352594–1352594. 4 indexed citations
8.
Bleckwehl, Tore, Anne Babler, Michael Nyberg, et al.. (2024). Encompassing view of spatial and single-cell RNA sequencing renews the role of the microvasculature in human atherosclerosis. Nature Cardiovascular Research. 4(1). 26–44. 19 indexed citations
9.
Das, Vivek, et al.. (2024). The Application of Artificial Intelligence in Computer Network Technology. International Journal of Advanced Research in Science Communication and Technology. 588–592.
10.
Kim, Young Chul, Vivek Das, Stephanie M. Stanford, et al.. (2024). Transcriptomics of SGLT2-positive early proximal tubule segments in mice: response to type 1 diabetes, SGLT1/2 inhibition, or GLP1 receptor agonism. American Journal of Physiology-Renal Physiology. 328(1). F68–F81. 1 indexed citations
11.
Das, Vivek, et al.. (2023). Unsupervised neural network for single cell Multi-omics INTegration (UMINT): an application to health and disease. Frontiers in Molecular Biosciences. 10. 1184748–1184748. 5 indexed citations
12.
Das, Vivek, et al.. (2023). UMINT-FS: UMINT-guided Feature Selection for multi-omics datasets. 594–601. 1 indexed citations
13.
Laise, Pasquale, James M. Hughes, Sebastiano Trattaro, et al.. (2022). EZH2-Mediated H3K27me3 Targets Transcriptional Circuits of Neuronal Differentiation. Frontiers in Neuroscience. 16. 814144–814144. 12 indexed citations
14.
Das, Vivek, et al.. (2022). CASSL: A cell-type annotation method for single cell transcriptomics data using semi-supervised learning. Applied Intelligence. 53(2). 1287–1305. 8 indexed citations
15.
Cox, Laura A., Jeremy P. Glenn, Vivek Das, et al.. (2021). Integrated omics analysis reveals sirtuin signaling is central to hepatic response to a high fructose diet. BMC Genomics. 22(1). 870–870. 6 indexed citations
16.
17.
Misra, Biswapriya B., Vivek Das, Marco Landi, Maria Rosa Abenavoli, & Fabrizio Araniti. (2020). Short-term effects of the allelochemical umbelliferone on Triticum durum L. metabolism through GC–MS based untargeted metabolomics. Plant Science. 298. 110548–110548. 25 indexed citations
18.
Krassowski, Michał, Vivek Das, S. Sahu, & Biswapriya B. Misra. (2020). State of the Field in Multi-Omics Research: From Computational Needs to Data Mining and Sharing. Frontiers in Genetics. 11. 610798–610798. 196 indexed citations
19.
Germain, Pierre‐Luc, Alessandro Vitriolo, Antonio Adamo, et al.. (2016). RNAontheBENCH: computational and empirical resources for benchmarking RNAseq quantification and differential expression methods. Nucleic Acids Research. 44(11). 5054–5067. 27 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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