David Gurwitz

9.8k total citations · 1 hit paper
193 papers, 5.8k citations indexed

About

David Gurwitz is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Genetics. According to data from OpenAlex, David Gurwitz has authored 193 papers receiving a total of 5.8k indexed citations (citations by other indexed papers that have themselves been cited), including 81 papers in Molecular Biology, 43 papers in Cellular and Molecular Neuroscience and 33 papers in Genetics. Recurrent topics in David Gurwitz's work include Receptor Mechanisms and Signaling (31 papers), Pharmacogenetics and Drug Metabolism (25 papers) and Neuroscience and Neuropharmacology Research (23 papers). David Gurwitz is often cited by papers focused on Receptor Mechanisms and Signaling (31 papers), Pharmacogenetics and Drug Metabolism (25 papers) and Neuroscience and Neuropharmacology Research (23 papers). David Gurwitz collaborates with scholars based in Israel, United States and United Kingdom. David Gurwitz's co-authors include Dennis D. Cunningham, Mordechai Sokolovsky, Moshe Rehavi, Abraham Fisher, Jeantine E. Lunshof, Abraham Weizman, Noam Shomron, Rachel Haring, K. P. Cavanaugh and Ralph Bradshaw and has published in prestigious journals such as Nature, Science and Proceedings of the National Academy of Sciences.

In The Last Decade

David Gurwitz

188 papers receiving 5.6k citations

Hit Papers

Angiotensin receptor bloc... 2020 2026 2022 2024 2020 100 200 300 400 500

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
David Gurwitz 2.4k 971 908 627 595 193 5.8k
Winfried Siffert 3.7k 1.5× 401 0.4× 1.2k 1.3× 218 0.3× 274 0.5× 262 8.4k
Motohiro Kato 1.7k 0.7× 841 0.9× 446 0.5× 125 0.2× 1.0k 1.7× 354 6.8k
Simon D Harding 2.9k 1.2× 1.2k 1.3× 258 0.3× 277 0.4× 176 0.3× 36 6.9k
Guillaume Paré 2.8k 1.2× 263 0.3× 2.7k 2.9× 251 0.4× 597 1.0× 268 10.6k
Yuan Ji 3.4k 1.4× 312 0.3× 616 0.7× 249 0.4× 157 0.3× 120 6.5k
Andrew D. Paterson 5.2k 2.2× 967 1.0× 3.9k 4.3× 214 0.3× 622 1.0× 329 12.9k
Gilberto Schwartsmann 2.4k 1.0× 836 0.9× 393 0.4× 123 0.2× 218 0.4× 244 6.2k
Jay A. Tischfield 6.8k 2.9× 1.9k 2.0× 2.1k 2.3× 268 0.4× 190 0.3× 258 12.2k
Florent Soubrier 3.6k 1.5× 636 0.7× 2.8k 3.1× 137 0.2× 503 0.8× 194 17.2k
Arlene R. Hughes 2.2k 0.9× 749 0.8× 1.1k 1.2× 521 0.8× 67 0.1× 48 7.6k

Countries citing papers authored by David Gurwitz

Since Specialization
Citations

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

Fields of papers citing papers by David Gurwitz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Gurwitz

This figure shows the co-authorship network connecting the top 25 collaborators of David Gurwitz. A scholar is included among the top collaborators of David Gurwitz 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 David Gurwitz. David Gurwitz 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.
Frye, Richard E., et al.. (2024). Nitrosative Stress in Autism: Supportive Evidence and Implications for Mitochondrial Dysfunction. Advanced Science. 11(16). e2304439–e2304439. 6 indexed citations
3.
Choudhary, Ashwani, Elena Milanesi, John R. Kelsoe, et al.. (2023). Immunoglobulin genes expressed in lymphoblastoid cell lines discern and predict lithium response in bipolar disorder patients. Molecular Psychiatry. 28(10). 4280–4293. 7 indexed citations
4.
Pan, Hope, Nir Pillar, Boaz Barak, et al.. (2023). Impaired myelin ultrastructure is reversed by citalopram treatment in a mouse model for major depressive disorder. Journal of Psychiatric Research. 166. 100–114. 3 indexed citations
5.
Yitzhaky, Assif, et al.. (2023). Meta-analysis of brain samples of individuals with schizophrenia detects down-regulation of multiple ATP synthase encoding genes in both females and males. Journal of Psychiatric Research. 158. 350–359. 9 indexed citations
6.
Hertzberg, Libi, Guy Shapira, Aviv Segev, et al.. (2021). Blood transcriptional response to treatment-resistant depression during electroconvulsive therapy. Journal of Psychiatric Research. 141. 92–103. 12 indexed citations
7.
Nagaraj, Karthik, Rive Sarfstein, David Gurwitz, et al.. (2018). Identification of thioredoxin-interacting protein (TXNIP) as a downstream target for IGF1 action. Proceedings of the National Academy of Sciences. 115(5). 1045–1050. 49 indexed citations
8.
Stacey, David, Klaus Oliver Schubert, Scott R. Clark, et al.. (2018). A gene co-expression module implicating the mitochondrial electron transport chain is associated with long-term response to lithium treatment in bipolar affective disorder. Translational Psychiatry. 8(1). 183–183. 21 indexed citations
10.
Fabbri, Chiara, Concetta Crisafulli, David Gurwitz, et al.. (2015). Neuronal cell adhesion genes and antidepressant response in three independent samples. The Pharmacogenomics Journal. 15(6). 538–548. 26 indexed citations
11.
Pasmanik‐Chor, Metsada, Varda Oron‐Karni, Noam Shomron, et al.. (2012). Genome-Wide miRNA Expression Profiling of Human Lymphoblastoid Cell Lines Identifies Tentative SSRI Antidepressant Response Biomarkers. Pharmacogenomics. 13(10). 1129–1139. 59 indexed citations
12.
Gurwitz, David, et al.. (2012). Sex Differences in Human Lymphoblastoid Cells Sensitivities to Antipsychotic Drugs. Journal of Molecular Neuroscience. 49(3). 554–558. 9 indexed citations
13.
Gurwitz, David, et al.. (2011). Decreased serotonin content and reduced agonist-induced aggregation in platelets of patients chronically medicated with SSRI drugs. Journal of Affective Disorders. 136(1-2). 99–103. 69 indexed citations
14.
Pasmanik‐Chor, Metsada, et al.. (2011). Genome-wide Expression Profiling of Human Lymphoblastoid Cell Lines Identifies CHL1 as a Putative SSRI Antidepressant Response Biomarker. Pharmacogenomics. 12(2). 171–184. 48 indexed citations
15.
Shavit, Eran, Amos D. Korczyn, Vivian E. Drory, et al.. (2008). Thrombin receptor PAR-1 on myelin at the node of Ranvier: a new anatomy and physiology of conduction block. Brain. 131(4). 1113–1122. 34 indexed citations
16.
Basit, Abdul W., David Gurwitz, Munir Pirmohamed, et al.. (2006). Invited Abstracts. Journal of Pharmacy and Pharmacology. 58(Supplement_1). A–91. 4 indexed citations
17.
Nakai, Kenji, Kenji Nakai, Wataru Habano, et al.. (2005). Ethnic differences in CYP2C9*2 (Arg144Cys) and CYP2C9*3 (Ile359Leu) genotypes in Japanese and Israeli populations. Life Sciences. 78(1). 107–111. 35 indexed citations
18.
Nakai, Kenji, Wataru Habano, Keiko Nakai, et al.. (2004). Ethnic differences of coronary artery disease-associated SNPs in two Israeli healthy populations using MALDI-TOF mass spectrometry. Life Sciences. 75(8). 1003–1010. 8 indexed citations
19.
Espey, Michael Graham, Anthony S. Basile, Robert K. Heaton, et al.. (2002). Giant axonal neuropathy (GAN): Case report and two novel mutations in the gigaxonin gene. Neurology. 58(9). 1444–1444. 2 indexed citations
20.
Gurwitz, David. (1999). Novel 5-HT1A-receptor agonists: F11440, MKC242 and BAYx3702. Drug Discovery Today. 4(3). 142–143. 11 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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