Ashkan Javaherian

3.3k total citations · 1 hit paper
18 papers, 2.0k citations indexed

About

Ashkan Javaherian is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Cell Biology. According to data from OpenAlex, Ashkan Javaherian has authored 18 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 6 papers in Cellular and Molecular Neuroscience and 5 papers in Cell Biology. Recurrent topics in Ashkan Javaherian's work include RNA Research and Splicing (4 papers), Neuroscience and Neuropharmacology Research (3 papers) and Neurogenetic and Muscular Disorders Research (3 papers). Ashkan Javaherian is often cited by papers focused on RNA Research and Splicing (4 papers), Neuroscience and Neuropharmacology Research (3 papers) and Neurogenetic and Muscular Disorders Research (3 papers). Ashkan Javaherian collaborates with scholars based in United States, Canada and Israel. Ashkan Javaherian's co-authors include Hollis T. Cline, Marica Gršković, George Q. Daley, Berta Strulovici, Arnold R. Kriegstein, Kurt Haas, Wun‐Chey Sin, Zheng Li, Steven Finkbeiner and Jonathan A. Garlick and has published in prestigious journals such as Nature, Cell and Nature Communications.

In The Last Decade

Ashkan Javaherian

18 papers receiving 2.0k citations

Hit Papers

In Silico Labeling: Predicting Fluorescent Labels in Unla... 2018 2026 2020 2023 2018 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ashkan Javaherian United States 15 1.2k 446 311 290 216 18 2.0k
Sunghoi Hong South Korea 22 1.5k 1.3× 516 1.2× 253 0.8× 431 1.5× 153 0.7× 63 2.1k
Hyuno Kang United States 11 1.2k 1.0× 950 2.1× 380 1.2× 200 0.7× 309 1.4× 12 2.4k
Gang Lin United States 15 1.2k 1.0× 399 0.9× 369 1.2× 143 0.5× 672 3.1× 30 2.4k
Spyros Darmanis United States 26 2.7k 2.3× 411 0.9× 279 0.9× 374 1.3× 319 1.5× 45 4.7k
Etsuo A. Susaki Japan 19 1.3k 1.1× 365 0.8× 907 2.9× 470 1.6× 87 0.4× 41 2.9k
Aleksandrina Goeva United States 7 2.3k 1.9× 300 0.7× 381 1.2× 112 0.4× 106 0.5× 12 3.0k
Payam Dibaj Germany 15 912 0.8× 275 0.6× 216 0.7× 153 0.5× 206 1.0× 38 1.7k
Sean Simmons United States 15 1.1k 1.0× 205 0.5× 88 0.3× 255 0.9× 241 1.1× 31 1.8k
Sagar Sagar Germany 18 1.3k 1.1× 221 0.5× 90 0.3× 97 0.3× 322 1.5× 51 3.7k
Francesca Peri Germany 19 1.1k 0.9× 379 0.8× 86 0.3× 110 0.4× 270 1.3× 28 2.7k

Countries citing papers authored by Ashkan Javaherian

Since Specialization
Citations

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

Fields of papers citing papers by Ashkan Javaherian

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ashkan Javaherian

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

All Works

18 of 18 papers shown
1.
Linsley, Jeremy W., Kevan Shah, Nicholas A. Castello, et al.. (2021). Genetically encoded cell-death indicators (GEDI) to detect an early irreversible commitment to neurodegeneration. Nature Communications. 12(1). 5284–5284. 17 indexed citations
2.
Linsley, Jeremy W., Drew Linsley, Kevan Shah, et al.. (2021). Superhuman cell death detection with biomarker-optimized neural networks. Science Advances. 7(50). eabf8142–eabf8142. 12 indexed citations
3.
Sathe, Shashank, Thai B. Nguyen, Neal Cody, et al.. (2021). Persistent mRNA localization defects and cell death in ALS neurons caused by transient cellular stress. Cell Reports. 36(10). 109685–109685. 23 indexed citations
4.
Markmiller, Sebastian, Shashank Sathe, Thai B. Nguyen, et al.. (2020). Persistent mRNA Localization Defects and Cell Death in ALS Neurons Caused by Transient Cellular Stress. SSRN Electronic Journal. 3 indexed citations
5.
Fang, Mark Y., Sebastian Markmiller, Anthony Q. Vu, et al.. (2019). Small-Molecule Modulation of TDP-43 Recruitment to Stress Granules Prevents Persistent TDP-43 Accumulation in ALS/FTD. Neuron. 103(5). 802–819.e11. 198 indexed citations
6.
Linsley, Jeremy W., Irina Epstein, Galina Schmunk, et al.. (2019). Automated four-dimensional long term imaging enables single cell tracking within organotypic brain slices to study neurodevelopment and degeneration. Communications Biology. 2(1). 155–155. 24 indexed citations
7.
Christiansen, Eric, Samuel Yang, D. Michael Ando, et al.. (2018). In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images. Cell. 173(3). 792–803.e19. 394 indexed citations breakdown →
8.
Lui, Jan H., Tomasz J. Nowakowski, Alex A. Pollen, et al.. (2014). Radial glia require PDGFD–PDGFRβ signalling in human but not mouse neocortex. Nature. 515(7526). 264–268. 120 indexed citations
9.
Javaherian, Ashkan, Roberto Bomprezzi, Layla Ghaffari, et al.. (2013). Identification of extracellular miRNA in human cerebrospinal fluid by next-generation sequencing. RNA. 19(5). 712–722. 153 indexed citations
10.
Gršković, Marica, Ashkan Javaherian, Berta Strulovici, & George Q. Daley. (2011). Induced pluripotent stem cells — opportunities for disease modelling and drug discovery. Nature Reviews Drug Discovery. 10(12). 915–929. 360 indexed citations
11.
Javaherian, Ashkan & Arnold R. Kriegstein. (2009). A Stem Cell Niche for Intermediate Progenitor Cells of the Embryonic Cortex. Cerebral Cortex. 19(suppl_1). i70–i77. 108 indexed citations
12.
Javaherian, Ashkan & Hollis T. Cline. (2005). Coordinated Motor Neuron Axon Growth and Neuromuscular Synaptogenesis Are Promoted by CPG15 In Vivo. Neuron. 45(4). 505–512. 109 indexed citations
13.
Javaherian, Ashkan, et al.. (2003). Use of Skin Equivalent Technology in a Wound Healing Model. Tissue Engineering. 18. 391–406. 3 indexed citations
14.
Haas, Kurt, Wun‐Chey Sin, Ashkan Javaherian, Zheng Li, & Hollis T. Cline. (2001). Single-Cell Electroporationfor Gene Transfer In Vivo. Neuron. 29(3). 583–591. 280 indexed citations
15.
Nedivi, Elly, Ashkan Javaherian, Isabel Cantallops, & Hollis T. Cline. (2001). Developmental regulation of CPG15 expression in Xenopus. The Journal of Comparative Neurology. 435(4). 464–473. 33 indexed citations
16.
Javaherian, Ashkan, et al.. (1999). Cell Interactions Control the Fate of Malignant Keratinocytes in an Organotypic Model of Early Neoplasia. Journal of Investigative Dermatology. 113(3). 384–391. 45 indexed citations
17.
Wang, Yanfang, et al.. (1999). 12-O-tetradecanoylphorbol-13-acetate induces clonal expansion of potentially malignant keratinocytes in a tissue model of early neoplastic progression.. PubMed. 59(2). 474–81. 34 indexed citations
18.
Javaherian, Ashkan, et al.. (1998). Normal keratinocytes suppress early stages of neoplastic progression in stratified epithelium.. PubMed. 58(10). 2200–8. 61 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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