David M. Rubitski

634 total citations
7 papers, 158 citations indexed

About

David M. Rubitski is a scholar working on Genetics, Molecular Biology and Pharmacology. According to data from OpenAlex, David M. Rubitski has authored 7 papers receiving a total of 158 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Genetics, 3 papers in Molecular Biology and 2 papers in Pharmacology. Recurrent topics in David M. Rubitski's work include Virus-based gene therapy research (3 papers), Viral Infectious Diseases and Gene Expression in Insects (2 papers) and Cholinesterase and Neurodegenerative Diseases (2 papers). David M. Rubitski is often cited by papers focused on Virus-based gene therapy research (3 papers), Viral Infectious Diseases and Gene Expression in Insects (2 papers) and Cholinesterase and Neurodegenerative Diseases (2 papers). David M. Rubitski collaborates with scholars based in United States. David M. Rubitski's co-authors include Katherine Fisher, Aarti Sawant‐Basak, K J Stutzman-Engwall, Sarah Grimwood, David E. Johnson, Laura McDowell, Elaine Tseng, Michelle Vanase‐Frawley, Matthew M. Hayward and Li Xu and has published in prestigious journals such as Journal of Medicinal Chemistry, Journal of Pharmacology and Experimental Therapeutics and Toxicology and Applied Pharmacology.

In The Last Decade

David M. Rubitski

7 papers receiving 155 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
David M. Rubitski United States	5	74	44	37	27	23	7	158
Kelli Jones United States	6	99 1.3×	46 1.0×	24 0.6×	11 0.4×	26 1.1×	9	160
Kelly Connolly United States	9	135 1.8×	140 3.2×	72 1.9×	14 0.5×	51 2.2×	13	311
Dean Paes Netherlands	10	216 2.9×	50 1.1×	104 2.8×	36 1.3×	13 0.6×	17	289
Claudia Albrecht Germany	7	146 2.0×	63 1.4×	51 1.4×	43 1.6×	6 0.3×	10	264
Ma. Reina Improgo United States	8	322 4.4×	97 2.2×	32 0.9×	50 1.9×	33 1.4×	12	401
Emily Miller United States	8	89 1.2×	91 2.1×	34 0.9×	32 1.2×	14 0.6×	13	242
Ken Yoshikawa Japan	7	230 3.1×	49 1.1×	41 1.1×	28 1.0×	6 0.3×	11	371
Mahsa Sadeghi Australia	11	181 2.4×	67 1.5×	45 1.2×	69 2.6×	6 0.3×	15	314
Andrew McMaster United Kingdom	6	77 1.0×	44 1.0×	18 0.5×	58 2.1×	20 0.9×	7	333
Pia Jensen Denmark	9	138 1.9×	51 1.2×	10 0.3×	40 1.5×	13 0.6×	16	253

Countries citing papers authored by David M. Rubitski

Since Specialization

Citations

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

Fields of papers citing papers by David M. Rubitski

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David M. Rubitski

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

All Works

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7 of 7 papers shown

Qiu, Luping, Steven W. Kumpf, Elias M. Oziolor, et al.. (2025). In vitro NIH3T3 mouse embryonic fibroblast cell model does not predict AAV2 or AAVdj-mediated cell transformation. Toxicology and Applied Pharmacology. 495. 117229–117229. 2 indexed citations

Kumpf, Steven W., et al.. (2024). Comparison and cross-validation of long-read and short-read target-enrichment sequencing methods to assess AAV vector integration into host genome. Molecular Therapy — Methods & Clinical Development. 32(4). 101352–101352. 1 indexed citations

Oziolor, Elias M., Steven W. Kumpf, Mark Gosink, et al.. (2023). Comparing molecular and computational approaches for detecting viral integration of AAV gene therapy constructs. Molecular Therapy — Methods & Clinical Development. 29. 395–405. 14 indexed citations

Wang, Qi, David M. Rubitski, Matthew M. Hayward, et al.. (2015). Identification of Phosphorylation Consensus Sequences and Endogenous Neuronal Substrates of the Psychiatric Risk Kinase TNIK. Journal of Pharmacology and Experimental Therapeutics. 356(2). 410–423. 29 indexed citations

Wager, Travis T., Ramalakshmi Y. Chandrasekaran, Jenifer A. Bradley, et al.. (2014). Casein Kinase 1δ/ε Inhibitor PF-5006739 Attenuates Opioid Drug-Seeking Behavior. ACS Chemical Neuroscience. 5(12). 1253–1265. 29 indexed citations

Johnson, David E., Sarah Grimwood, Aarti Sawant‐Basak, et al.. (2012). The 5-Hydroxytryptamine4 Receptor Agonists Prucalopride and PRX-03140 Increase Acetylcholine and Histamine Levels in the Rat Prefrontal Cortex and the Power of Stimulated Hippocampal θ Oscillations. Journal of Pharmacology and Experimental Therapeutics. 341(3). 681–691. 47 indexed citations

Brodney, Michael A., David E. Johnson, Aarti Sawant‐Basak, et al.. (2012). Identification of Multiple 5-HT₄Partial Agonist Clinical Candidates for the Treatment of Alzheimer’s Disease. Journal of Medicinal Chemistry. 55(21). 9240–9254. 36 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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