Pradeep Narayanaswamy

1.3k total citations · 1 hit paper
13 papers, 918 citations indexed

About

Pradeep Narayanaswamy is a scholar working on Molecular Biology, Cell Biology and Oncology. According to data from OpenAlex, Pradeep Narayanaswamy has authored 13 papers receiving a total of 918 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 3 papers in Cell Biology and 2 papers in Oncology. Recurrent topics in Pradeep Narayanaswamy's work include Sphingolipid Metabolism and Signaling (5 papers), Lipid Membrane Structure and Behavior (4 papers) and Cellular transport and secretion (3 papers). Pradeep Narayanaswamy is often cited by papers focused on Sphingolipid Metabolism and Signaling (5 papers), Lipid Membrane Structure and Behavior (4 papers) and Cellular transport and secretion (3 papers). Pradeep Narayanaswamy collaborates with scholars based in Singapore, United States and Germany. Pradeep Narayanaswamy's co-authors include Federico Torta, Markus R. Wenk, Pietro De Camilli, Kaori Masai, Jeeyun Chung, Lukas B. Tanner, Louise Lucast, Fubito Nakatsu, Yasunori Saheki and Xudong Wu and has published in prestigious journals such as Nature, Science and Analytical Chemistry.

In The Last Decade

Pradeep Narayanaswamy

13 papers receiving 915 citations

Hit Papers

PI4P/phosphatidylserine c... 2015 2026 2018 2022 2015 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. PI4P/phosphatidylserine countertransport at ORP5- and ORP8-mediated ER–plasma membrane contacts
    2015 475 citations Jeeyun Chung, Federico Torta et al. Science profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pradeep Narayanaswamy Singapore 10 695 437 108 97 70 13 918
Andrew Murley United States 7 1.0k 1.4× 319 0.7× 88 0.8× 104 1.1× 32 0.5× 9 1.2k
Kaori Masai Japan 2 355 0.5× 271 0.6× 56 0.5× 50 0.5× 58 0.8× 6 488
Dániel J. Tóth Hungary 15 529 0.8× 319 0.7× 32 0.3× 66 0.7× 62 0.9× 21 803
Holger Lorenz Germany 17 1.0k 1.5× 411 0.9× 44 0.4× 122 1.3× 40 0.6× 27 1.4k
Gil Kanfer Switzerland 10 566 0.8× 269 0.6× 48 0.4× 65 0.7× 23 0.3× 11 757
Michal Eisenberg‐Bord Israel 13 765 1.1× 300 0.7× 219 2.0× 109 1.1× 35 0.5× 14 962
Thomas Strahl United States 12 829 1.2× 433 1.0× 35 0.3× 59 0.6× 40 0.6× 14 1.0k
Daniel Baird United States 5 430 0.6× 351 0.8× 33 0.3× 31 0.3× 39 0.6× 6 560
Christopher J. Guerriero United States 18 697 1.0× 676 1.5× 18 0.2× 143 1.5× 98 1.4× 32 1.2k
T. Tony Yang Taiwan 12 678 1.0× 450 1.0× 27 0.3× 73 0.8× 28 0.4× 20 903

Countries citing papers authored by Pradeep Narayanaswamy

Since Specialization
Citations

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

Fields of papers citing papers by Pradeep Narayanaswamy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pradeep Narayanaswamy

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

All Works

13 of 13 papers shown
1.
Muralidharan, Sneha, Qing Zhao, Iris D. Zelnik, et al.. (2025). Deep sphingolipidomic and metabolomic analyses of ceramide synthase 2 null mice reveal complex pathway-specific effects. Journal of Lipid Research. 66(7). 100832–100832. 1 indexed citations
2.
Castaño, David, Francesco Girolamo, Laia Trigueros‐Motos, et al.. (2021). Loss of ABCA8B decreases myelination by reducing oligodendrocyte precursor cells in mice. Journal of Lipid Research. 63(1). 100147–100147. 8 indexed citations
3.
Khanapur, Shivashankar, Fui‐Fong Yong, Siddesh V. Hartimath, et al.. (2021). An Improved Synthesis of N-(4-[18F]Fluorobenzoyl)-Interleukin-2 for the Preclinical PET Imaging of Tumour-Infiltrating T-cells in CT26 and MC38 Colon Cancer Models. Molecules. 26(6). 1728–1728. 3 indexed citations
4.
Narayanaswamy, Pradeep, Guoshou Teo, Jin Rong Ow, et al.. (2020). MetaboKit: a comprehensive data extraction tool for untargeted metabolomics. Molecular Omics. 16(5). 436–447. 14 indexed citations
5.
Thiam, Chung Hwee, Kar Wai Tan, Federico Torta, et al.. (2019). Halted Lymphocyte Egress via Efferent Lymph Contributes to Lymph Node Hypertrophy During Hypercholesterolemia. Frontiers in Immunology. 10. 575–575. 12 indexed citations
6.
Begum, Husna, Federico Torta, Pradeep Narayanaswamy, et al.. (2017). Lipidomic profiling of plasma in a healthy Singaporean population to identify ethnic specific differences in lipid levels and associations with disease risk factors. PubMed. 6. 25–31. 9 indexed citations
7.
Lopes, Victor, Wenbin Wei, Maha Ibrahim, et al.. (2016). Aberrant expression of the S1P regulating enzymes, SPHK1 and SGPL1, contributes to a migratory phenotype in OSCC mediated through S1PR2. Scientific Reports. 6(1). 25650–25650. 36 indexed citations
8.
Chung, Jeeyun, Federico Torta, Kaori Masai, et al.. (2015). PI4P/phosphatidylserine countertransport at ORP5- and ORP8-mediated ER–plasma membrane contacts. Science. 349(6246). 428–432. 475 indexed citations breakdown →
9.
Hebbar, Sarita, Artur Matysik, Kathleen Amy Osborne, et al.. (2015). Ceramides And Stress Signalling Intersect With Autophagic Defects In Neurodegenerative Drosophila blue cheese (bchs) Mutants. Scientific Reports. 5(1). 15926–15926. 18 indexed citations
10.
Schauder, Curtis, Xudong Wu, Yasunori Saheki, et al.. (2014). Structure of a lipid-bound extended synaptotagmin indicates a role in lipid transfer. Nature. 510(7506). 552–555. 246 indexed citations
11.
Narayanaswamy, Pradeep, Sudhirkumar Shinde, Rachel Kraut, et al.. (2014). Lipidomic “Deep Profiling”: An Enhanced Workflow to Reveal New Molecular Species of Signaling Lipids. Analytical Chemistry. 86(6). 3043–3047. 68 indexed citations
12.
Narayanaswamy, Pradeep, et al.. (2009). A tandem mass spectrometric approach to the identification of O‐glycosylated glargine glycoforms in active pharmaceutical ingredient expressed in Pichia pastoris. Rapid Communications in Mass Spectrometry. 23(7). 1035–1042. 18 indexed citations
13.
Narayanaswamy, Pradeep, C. R. Bector, & Divakar Rajamani. (1996). Fuzzy logic concepts applied to machine—component matrix formation in cellular manufacturing. European Journal of Operational Research. 93(1). 88–97. 10 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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