Vikram Khurana

5.9k total citations · 1 hit paper
44 papers, 2.6k citations indexed

About

Vikram Khurana is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Neurology. According to data from OpenAlex, Vikram Khurana has authored 44 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Molecular Biology, 18 papers in Cellular and Molecular Neuroscience and 16 papers in Neurology. Recurrent topics in Vikram Khurana's work include Parkinson's Disease Mechanisms and Treatments (14 papers), Genetic Neurodegenerative Diseases (9 papers) and CRISPR and Genetic Engineering (5 papers). Vikram Khurana is often cited by papers focused on Parkinson's Disease Mechanisms and Treatments (14 papers), Genetic Neurodegenerative Diseases (9 papers) and CRISPR and Genetic Engineering (5 papers). Vikram Khurana collaborates with scholars based in United States, Australia and Spain. Vikram Khurana's co-authors include Susan Lindquist, Mel Β. Feany, Michelle Leigh Steinhilb, Tudor A. Fulga, Ilan Elson‐Schwab, Tara L. Spires‐Jones, Bradley T. Hyman, Chee Yeun Chung, Cathrin M. Bütefisch and Leonardo G. Cohen and has published in prestigious journals such as Science, New England Journal of Medicine and Cell.

In The Last Decade

Vikram Khurana

42 papers receiving 2.6k citations

Hit Papers

Generation of Isogenic Pluripotent Stem Cells Differing E... 2011 2026 2016 2021 2011 100 200 300 400 500

Peers

Vikram Khurana
Chunni Zhu United States
Leah Boyer United States
Hyemyung Seo South Korea
Smita Saxena Switzerland
Hibiki Kawamata United States
Enrico Zampese United States
Marcel van der Brug United States
Chunni Zhu United States
Vikram Khurana
Citations per year, relative to Vikram Khurana Vikram Khurana (= 1×) peers Chunni Zhu

Countries citing papers authored by Vikram Khurana

Since Specialization
Citations

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

Fields of papers citing papers by Vikram Khurana

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vikram Khurana

This figure shows the co-authorship network connecting the top 25 collaborators of Vikram Khurana. A scholar is included among the top collaborators of Vikram Khurana 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 Vikram Khurana. Vikram Khurana 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.
Zhang, Yanhong, Rosalia Rabinovsky, Anastasia Kuzkina, et al.. (2025). Lipid nanoparticle formulation for gene editing and RNA-based therapies for glioblastoma. Neuro-Oncology. 27(11). 2812–2827. 4 indexed citations
2.
Baluapuri, Apoorva, Ning Zhao, Ryan J. Marina, et al.. (2025). Integrator loss leads to dsRNA formation that triggers the integrated stress response. Cell. 188(12). 3184–3201.e21. 5 indexed citations
3.
Naseri, Nima, Sarshan R. Pather, Erinc Hallacli, et al.. (2024). Sequential CRISPR screening reveals partial NatB inhibition as a strategy to mitigate alpha-synuclein levels in human neurons. Science Advances. 10(6). eadj4767–eadj4767. 5 indexed citations
4.
Krans, Amy, Andrea Suárez, Alan P. Boyle, et al.. (2024). AAGGG repeat expansions trigger RFC1 -independent synaptic dysregulation in human CANVAS neurons. Science Advances. 10(36). eadn2321–eadn2321. 5 indexed citations
5.
Ndayisaba, Alain, Glenda M. Halliday, & Vikram Khurana. (2024). Multiple System Atrophy: Pathology, Pathogenesis, and Path Forward. Annual Review of Pathology Mechanisms of Disease. 20(1). 245–273. 5 indexed citations
6.
Grant, Natalie, Jin Yun Chen, Precilla D’Souza, et al.. (2024). The spectrum of neurological presentation in individuals affected by TBL1XR1 gene defects. Orphanet Journal of Rare Diseases. 19(1). 79–79. 2 indexed citations
7.
Paul, Kimberly C., Richard C. Krolewski, Edinson Lucumi Moreno, et al.. (2023). A pesticide and iPSC dopaminergic neuron screen identifies and classifies Parkinson-relevant pesticides. Nature Communications. 14(1). 2803–2803. 50 indexed citations
8.
Burke, Christopher J., Xin Jiang, Ping Xu, et al.. (2022). Patient-derived three-dimensional cortical neurospheres to model Parkinson’s disease. PLoS ONE. 17(12). e0277532–e0277532. 6 indexed citations
9.
Nuber, Silke, Chee Yeun Chung, Daniel F. Tardiff, et al.. (2022). A Brain-Penetrant Stearoyl-CoA Desaturase Inhibitor Reverses α-Synuclein Toxicity. Neurotherapeutics. 19(3). 1018–1036. 11 indexed citations
10.
Murueta‐Goyena, Ane, Raffaela Cipriani, Mar Carmona‐Abellán, et al.. (2022). Characterization of molecular biomarkers in cerebrospinal fluid and serum of E46K-SNCA mutation carriers. Parkinsonism & Related Disorders. 96. 29–35. 2 indexed citations
11.
Wong, Darice Y., Claudio M. de Gusmão, May Sanyoura, et al.. (2020). Prevalence of RFC1 -mediated spinocerebellar ataxia in a North American ataxia cohort. Neurology Genetics. 6(3). e440–e440. 32 indexed citations
12.
Wong, Darice Y., Claudio M. de Gusmão, May Sanyoura, et al.. (2020). Prevalence of RFC1-Mediated Spinocerebellar Ataxia in a North American Ataxia Cohort. Annals of Neurology. 88. 3 indexed citations
13.
Lam, Isabel, Erinc Hallacli, & Vikram Khurana. (2019). Proteome-Scale Mapping of Perturbed Proteostasis in Living Cells. Cold Spring Harbor Perspectives in Biology. 12(2). a034124–a034124. 3 indexed citations
14.
Vincent, Benjamin, Daniel F. Tardiff, Jeff S. Piotrowski, et al.. (2018). Inhibiting Stearoyl-CoA Desaturase Ameliorates α-Synuclein Cytotoxicity. Cell Reports. 25(10). 2742–2754.e31. 102 indexed citations
15.
Tardiff, Daniel F., Nathan T. Jui, Vikram Khurana, et al.. (2013). Yeast Reveal a “Druggable” Rsp5/Nedd4 Network that Ameliorates α-Synuclein Toxicity in Neurons. Science. 342(6161). 979–983. 200 indexed citations
16.
Jui, Nathan T., Stephen L. Buchwald, Susan Lindquist, et al.. (2013). Yeast Reveal a ‘Druggable’ Rsp5/Nedd4 Network That Ameliorates α-Synuclein Toxicity in Neurons. DSpace@MIT (Massachusetts Institute of Technology). 2 indexed citations
17.
Soldner, Frank, Josée Laganière, Albert Cheng, et al.. (2011). Generation of Isogenic Pluripotent Stem Cells Differing Exclusively at Two Early Onset Parkinson Point Mutations. Cell. 146(2). 318–331. 555 indexed citations breakdown →
18.
Khurana, Vikram, Paola Merlo, Tudor A. Fulga, et al.. (2011). A neuroprotective role for the DNA damage checkpoint in tauopathy. Aging Cell. 11(2). 360–362. 46 indexed citations
19.
McLeod, J. G., John D. Pollard, Petra Macaskill, et al.. (1999). Prevalence of chronic inflammatory demyelinating polyneuropathy in New South Wales, Australia. Annals of Neurology. 46(6). 910–913. 154 indexed citations
20.
Macleod, Gregory T., Vikram Khurana, W.G. Gibson, & Maxwell R. Bennett. (1998). Probability of Quantal Secretion and the Mobilization of Vesicles at the Active Zones of Endplates. Journal of Theoretical Biology. 191(3). 323–334. 5 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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