K.S. Krishnan

2.0k total citations
50 papers, 1.7k citations indexed

About

K.S. Krishnan is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Cell Biology. According to data from OpenAlex, K.S. Krishnan has authored 50 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Molecular Biology, 23 papers in Cellular and Molecular Neuroscience and 21 papers in Cell Biology. Recurrent topics in K.S. Krishnan's work include Cellular transport and secretion (21 papers), Neurobiology and Insect Physiology Research (20 papers) and Lipid Membrane Structure and Behavior (16 papers). K.S. Krishnan is often cited by papers focused on Cellular transport and secretion (21 papers), Neurobiology and Insect Physiology Research (20 papers) and Lipid Membrane Structure and Behavior (16 papers). K.S. Krishnan collaborates with scholars based in India, United States and Ireland. K.S. Krishnan's co-authors include Mani Ramaswami, Regis B. Kelly, Venkataraman Sriram, Satyajit Mayor, Patricia S. Estes, H A Nash, Richa Rikhy, John F. Brandts, Subhabrata Sanyal and P. Balaram and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Neuron.

In The Last Decade

K.S. Krishnan

50 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
K.S. Krishnan India 26 1.2k 753 666 139 103 50 1.7k
Andrea Daga Italy 21 1.5k 1.2× 741 1.0× 710 1.1× 158 1.1× 154 1.5× 30 2.3k
Robert M. Tombes United States 29 1.6k 1.3× 660 0.9× 330 0.5× 173 1.2× 207 2.0× 55 2.8k
Vadim A. Klenchin United States 24 1.6k 1.4× 857 1.1× 523 0.8× 130 0.9× 94 0.9× 36 2.5k
Shohei Maékawa Japan 25 1.4k 1.2× 1.1k 1.5× 384 0.6× 183 1.3× 112 1.1× 89 2.2k
Brooke J. Bevis United States 12 1.5k 1.2× 1.0k 1.3× 306 0.5× 244 1.8× 134 1.3× 15 2.1k
Elise F. Stanley Canada 18 965 0.8× 373 0.5× 656 1.0× 234 1.7× 56 0.5× 34 1.6k
Margaret H. Butler United States 21 2.5k 2.1× 1.2k 1.6× 733 1.1× 335 2.4× 147 1.4× 27 3.2k
Patrick J. Dolph United States 21 1.4k 1.2× 388 0.5× 776 1.2× 66 0.5× 245 2.4× 36 1.9k
Joseph E. O’Tousa United States 26 1.7k 1.5× 655 0.9× 1.5k 2.3× 68 0.5× 236 2.3× 48 2.5k
Jonathan R. Terman United States 25 1.4k 1.2× 1.1k 1.4× 1.4k 2.1× 132 0.9× 72 0.7× 53 2.8k

Countries citing papers authored by K.S. Krishnan

Since Specialization
Citations

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

Fields of papers citing papers by K.S. Krishnan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by K.S. Krishnan. 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 K.S. Krishnan. The network helps show where K.S. Krishnan may publish in the future.

Co-authorship network of co-authors of K.S. Krishnan

This figure shows the co-authorship network connecting the top 25 collaborators of K.S. Krishnan. A scholar is included among the top collaborators of K.S. Krishnan 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 K.S. Krishnan. K.S. Krishnan 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.
Sanyal, Subhabrata & K.S. Krishnan. (2012). Genetic Modifiers of comatose Mutations in Drosophila : Insights Into Neuronal NSF ( N -Ethylmaleimide–Sensitive Fusion Factor) Functions. Journal of Neurogenetics. 26(3-4). 348–359. 2 indexed citations
2.
Kumar, Vimlesh, Robert Fricke, Suneel Reddy‐Alla, et al.. (2009). Syndapin Promotes Formation of a Postsynaptic Membrane System in Drosophila. Molecular Biology of the Cell. 20(8). 2254–2264. 39 indexed citations
3.
Kumar, Vimlesh, et al.. (2008). Syndapin is dispensable for synaptic vesicle endocytosis at the Drosophila larval neuromuscular junction. Molecular and Cellular Neuroscience. 40(2). 234–241. 30 indexed citations
4.
Gowd, Konkallu Hanumae, K.S. Krishnan, & P. Balaram. (2007). Identification of Conus amadis disulfide isomerase: minimum sequence length of peptide fragments necessary for protein annotation. Molecular BioSystems. 3(8). 554–566. 17 indexed citations
5.
6.
Gowd, Konkallu Hanumae, Varatharajan Sabareesh, Sudarslal Sadasivan Nair, et al.. (2005). Novel Peptides of Therapeutic Promise from Indian Conidae. Annals of the New York Academy of Sciences. 1056(1). 462–473. 25 indexed citations
7.
Sarma, Siddhartha P., Ganesan Senthil Kumar, Sudarslal Sadasivan Nair, et al.. (2005). Solution Structure of?-Am2766: A Highly Hydrophobic?-Conotoxin fromConus amadis That Inhibits Inactivation of Neuronal Voltage-Gated Sodium Channels. Chemistry & Biodiversity. 2(4). 535–556. 12 indexed citations
8.
Nair, Sudarslal Sadasivan, et al.. (2004). A novel 13 residue acyclic peptide from the marine snail, Conus monile, targets potassium channels. Biochemical and Biophysical Research Communications. 317(3). 682–688. 30 indexed citations
9.
Sriram, Venkataraman, K.S. Krishnan, & Satyajit Mayor. (2003). deep-orange and carnation define distinct stages in late endosomal biogenesis in Drosophila melanogaster. The Journal of Cell Biology. 161(3). 593–607. 84 indexed citations
10.
11.
Sanyal, Subhabrata & K.S. Krishnan. (2001). Lethal comatose mutation in Drosophila reveals possible role for NSF in neurogenesis. Neuroreport. 12(7). 1363–1366. 15 indexed citations
12.
13.
Krishnan, K.S., Richa Rikhy, Radhakrishnan Narayanan, et al.. (2001). Nucleoside Diphosphate Kinase, a Source of GTP, Is Required for Dynamin-Dependent Synaptic Vesicle Recycling. Neuron. 30(1). 197–210. 136 indexed citations
14.
Estes, Patricia S., et al.. (1996). Traffic of Dynamin within IndividualDrosophilaSynaptic Boutons Relative to Compartment-Specific Markers. Journal of Neuroscience. 16(17). 5443–5456. 149 indexed citations
15.
Leibovitch, Boris A., Jerry L. Campbell, K.S. Krishnan, & H A Nash. (1995). Mutations That Affect Ion Channels Change the Sensitivity ofDrosophila Melanogasterto Volatile Anesthetics. Journal of Neurogenetics. 10(1). 1–13. 35 indexed citations
16.
Pallanck, Leo J., et al.. (1995). Distinct Roles for N-Ethylmaleimide-sensitive Fusion Protein (NSF) Suggested by the Identification of a Second Drosophila NSF Homolog. Journal of Biological Chemistry. 270(32). 18742–18744. 49 indexed citations
17.
Ramaswami, Mani, K.S. Krishnan, & Regis B. Kelly. (1994). Intermediates in synaptic vesicle recycling revealed by optical imaging of Drosophila neuromuscular junctions. Neuron. 13(2). 363–375. 167 indexed citations
18.
Ramaswami, Mani, et al.. (1993). Genetic Studies on Dynamin Function inDrosophila. Journal of Neurogenetics. 9(2). 73–87. 54 indexed citations
19.
Krishnan, K.S. & H A Nash. (1990). A genetic study of the anesthetic response: mutants of Drosophila melanogaster altered in sensitivity to halothane.. Proceedings of the National Academy of Sciences. 87(21). 8632–8636. 64 indexed citations
20.
Krishnan, K.S. & John F. Brandts. (1978). [1] Scanning calorimetry. Methods in enzymology on CD-ROM/Methods in enzymology. 3–14. 55 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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