John W. Cave

2.7k total citations · 1 hit paper
43 papers, 1.9k citations indexed

About

John W. Cave is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Developmental Neuroscience. According to data from OpenAlex, John W. Cave has authored 43 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 12 papers in Cellular and Molecular Neuroscience and 12 papers in Developmental Neuroscience. Recurrent topics in John W. Cave's work include Neurogenesis and neuroplasticity mechanisms (11 papers), Neuroinflammation and Neurodegeneration Mechanisms (9 papers) and Nuclear Receptors and Signaling (8 papers). John W. Cave is often cited by papers focused on Neurogenesis and neuroplasticity mechanisms (11 papers), Neuroinflammation and Neurodegeneration Mechanisms (9 papers) and Nuclear Receptors and Signaling (8 papers). John W. Cave collaborates with scholars based in United States, United Kingdom and Japan. John W. Cave's co-authors include Harriet Baker, Maureen Owen, C. J. Joyner, Rajiv R. Ratan, Saravanan S. Karuppagounder, Ishraq Alim, Javier Seravalli, Botir T. Sagdullaev, Daniel H. Geschwind and Emma J. Ste.Marie and has published in prestigious journals such as Cell, Nature Communications and Journal of Neuroscience.

In The Last Decade

John W. Cave

42 papers receiving 1.9k citations

Hit Papers

Selenium Drives a Transcriptional Adaptive Program to Blo... 2019 2026 2021 2023 2019 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
John W. Cave United States 20 955 529 416 226 186 43 1.9k
Shouhong Xuan United States 24 2.5k 2.6× 143 0.3× 384 0.9× 242 1.1× 174 0.9× 32 4.4k
Matthew Plotkin United States 15 1.1k 1.1× 221 0.4× 82 0.2× 452 2.0× 180 1.0× 23 1.6k
Annika Keller Switzerland 19 1.0k 1.1× 192 0.4× 172 0.4× 261 1.2× 115 0.6× 38 2.5k
Petra Fallier‐Becker Germany 23 979 1.0× 190 0.4× 140 0.3× 303 1.3× 170 0.9× 50 2.4k
Koji Ando Japan 20 1.4k 1.5× 178 0.3× 240 0.6× 274 1.2× 125 0.7× 28 2.7k
Barbara Seidler Germany 23 1.0k 1.1× 103 0.2× 209 0.5× 136 0.6× 104 0.6× 41 1.8k
Susan Noell Germany 21 717 0.8× 278 0.5× 141 0.3× 248 1.1× 354 1.9× 33 1.8k
Annette Hammes Germany 19 1.2k 1.3× 190 0.4× 96 0.2× 147 0.7× 38 0.2× 33 2.1k
Kevin L. Stark United States 19 3.4k 3.5× 369 0.7× 280 0.7× 328 1.5× 119 0.6× 26 4.6k
Yoshinori Tsurusaki Japan 37 2.6k 2.7× 105 0.2× 236 0.6× 357 1.6× 190 1.0× 146 3.9k

Countries citing papers authored by John W. Cave

Since Specialization
Citations

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

Fields of papers citing papers by John W. Cave

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John W. Cave. 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 John W. Cave. The network helps show where John W. Cave may publish in the future.

Co-authorship network of co-authors of John W. Cave

This figure shows the co-authorship network connecting the top 25 collaborators of John W. Cave. A scholar is included among the top collaborators of John W. Cave 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 John W. Cave. John W. Cave 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.
Kim, Il‐Doo, Ina Pavlova, Shang Mu, et al.. (2025). Ischemic Conditioning Promotes Transneuronal Survival and Stroke Recovery via CD36-Mediated Efferocytosis. Circulation Research. 136(5). e34–e51.
2.
David, Brian T., Jennifer L. Brown, Saravanan S. Karuppagounder, et al.. (2022). Temporary induction of hypoxic adaptations by preconditioning fails to enhance Schwann cell transplant survival after spinal cord injury. Glia. 71(3). 648–666. 4 indexed citations
3.
Wang, Meng, et al.. (2018). Conserved Upstream Regulatory Regions in Mammalian Tyrosine Hydroxylase. Molecular Neurobiology. 55(9). 7340–7351. 1 indexed citations
4.
Cave, John W., J. Kenneth Wickiser, & Alexander N. Mitropoulos. (2018). Progress in the development of olfactory-based bioelectronic chemosensors. Biosensors and Bioelectronics. 123. 211–222. 47 indexed citations
5.
Ding, Baojin, Paul R. Dobner, Debra Mullikin-Kilpatrick, et al.. (2018). BDNF activates an NFI-dependent neurodevelopmental timing program by sequestering NFATc4. Molecular Biology of the Cell. 29(8). 975–987. 13 indexed citations
6.
Cave, John W., et al.. (2016). Partial Conservation between Mice and Humans in Olfactory Bulb Interneuron Transcription Factor Codes. Frontiers in Neuroscience. 10. 337–337. 14 indexed citations
7.
Cave, John W., et al.. (2016). Cytoarchitectural changes in the olfactory bulb of Parkinson’s disease patients. npj Parkinson s Disease. 2(1). 16011–16011. 18 indexed citations
9.
Mazzola, Michael, et al.. (2015). TMPyP4, a Stabilizer of Nucleic Acid Secondary Structure, Is a Novel Acetylcholinesterase Inhibitor. PLoS ONE. 10(9). e0139167–e0139167. 10 indexed citations
10.
Banerjee, Kasturi, et al.. (2014). Nucleotide sequence conservation of novel and established cis-regulatory sites within the tyrosine hydroxylase gene promoter. Frontiers in Biology. 10(1). 74–90. 7 indexed citations
11.
Banerjee, Kasturi, et al.. (2014). Regulation of tyrosine hydroxylase transcription by hnRNP K and DNA secondary structure. Nature Communications. 5(1). 5769–5769. 37 indexed citations
12.
Cave, John W.. (2011). Selective repression of Notch pathway target gene transcription. Developmental Biology. 360(1). 123–131. 9 indexed citations
13.
Cave, John W., Li Xia, & Michael Caudy. (2010). Differential Regulation of Transcription through Distinct Suppressor of Hairless DNA Binding Site Architectures during Notch Signaling in Proneural Clusters. Molecular and Cellular Biology. 31(1). 22–29. 9 indexed citations
14.
Cave, John W., et al.. (2010). Differential Regulation of Dopaminergic Gene Expression byEr81. Journal of Neuroscience. 30(13). 4717–4724. 37 indexed citations
15.
Akiba, Yosuke, et al.. (2010). Histone deacetylase inhibitors de-repress tyrosine hydroxylase expression in the olfactory bulb and rostral migratory stream. Biochemical and Biophysical Research Communications. 393(4). 673–677. 12 indexed citations
16.
Cave, John W., Li Xia, & Michael Caudy. (2009). The Daughterless N-terminus directly mediates synergistic interactions with Notch transcription complexes via the SPS+A DNA transcription code. BMC Research Notes. 2(1). 65–65. 7 indexed citations
17.
Cave, John W. & Michael Caudy. (2008). Promoter-specific co-activation by Drosophila mastermind. Biochemical and Biophysical Research Communications. 377(2). 658–661. 6 indexed citations
18.
Cave, John W., et al.. (2005). A DNA Transcription Code for Cell-Specific Gene Activation by Notch Signaling. Current Biology. 15(2). 94–104. 67 indexed citations
19.
Cave, John W., et al.. (2001). Solution nuclear magnetic resonance structure of a protein disulfide oxidoreductase from Methanococcus jannaschii. Protein Science. 10(2). 384–396. 11 indexed citations
20.
Owen, Maureen, et al.. (1984). EXPRESSION OF ALKALINE-PHOSPHATASE ACTIVITY IN INVITRO CULTURES OF MARROW STROMAL CELLS. Calcified Tissue International. 36. 467–467. 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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