Alan F. Rubin

3.0k total citations
35 papers, 930 citations indexed

About

Alan F. Rubin is a scholar working on Molecular Biology, Genetics and Cancer Research. According to data from OpenAlex, Alan F. Rubin has authored 35 papers receiving a total of 930 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Molecular Biology, 18 papers in Genetics and 5 papers in Cancer Research. Recurrent topics in Alan F. Rubin's work include Genomics and Rare Diseases (13 papers), Genomics and Phylogenetic Studies (6 papers) and Evolution and Genetic Dynamics (6 papers). Alan F. Rubin is often cited by papers focused on Genomics and Rare Diseases (13 papers), Genomics and Phylogenetic Studies (6 papers) and Evolution and Genetic Dynamics (6 papers). Alan F. Rubin collaborates with scholars based in Australia, United States and United Kingdom. Alan F. Rubin's co-authors include Douglas M. Fowler, Anthony T. Papenfuss, Phil Green, Lea M. Starita, D J Evans, Hannah Gelman, Jay Shendure, Terence P. Speed, Nathan Lucas and Frederick P. Roth and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Blood.

In The Last Decade

Alan F. Rubin

34 papers receiving 915 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Alan F. Rubin Australia	15	542	335	108	68	65	35	930
Kazuyoshi Ishigaki Japan	22	467 0.9×	431 1.3×	113 1.0×	26 0.4×	58 0.9×	49	1.3k
Takuma Hayashi Japan	24	755 1.4×	255 0.8×	276 2.6×	53 0.8×	44 0.7×	107	1.7k
Camari Ferguson United States	14	710 1.3×	221 0.7×	322 3.0×	51 0.8×	43 0.7×	16	1.6k
Olaf Hellwinkel Germany	17	576 1.1×	249 0.7×	244 2.3×	36 0.5×	45 0.7×	37	1.1k
Jane M. Turbov United States	14	515 1.0×	104 0.3×	143 1.3×	65 1.0×	74 1.1×	19	1.0k
Thandi M. Onami United States	15	591 1.1×	252 0.8×	56 0.5×	20 0.3×	41 0.6×	21	2.0k
Jingxuan Shan Qatar	14	431 0.8×	195 0.6×	243 2.3×	51 0.8×	35 0.5×	33	961
Deke Jiang China	22	668 1.2×	212 0.6×	397 3.7×	35 0.5×	28 0.4×	81	1.4k
Sean P. Bohen United States	11	879 1.6×	147 0.4×	59 0.5×	103 1.5×	35 0.5×	14	2.3k

Countries citing papers authored by Alan F. Rubin

Since Specialization

Citations

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

Fields of papers citing papers by Alan F. Rubin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alan F. Rubin. 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 Alan F. Rubin. The network helps show where Alan F. Rubin may publish in the future.

Co-authorship network of co-authors of Alan F. Rubin

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

All Works

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20 of 20 papers shown

Phipson, Belinda, et al.. (2025). Variant scoring tools for deep mutational scanning. Molecular Systems Biology. 21(10). 1293–1305. 1 indexed citations

Wu, Xiaoping, Shawn Fayer, Alan F. Rubin, et al.. (2025). Multiplex and multimodal mapping of variant effects in secreted proteins via MultiSTEP. Nature Structural & Molecular Biology. 32(10). 2099–2111. 2 indexed citations

Garrett, Alice, Lara A. Muffley, Shawn Fayer, et al.. (2024). Workshop report: the clinical application of data from multiplex assays of variant effect (MAVEs), 12 July 2023. European Journal of Human Genetics. 32(5). 593–600. 8 indexed citations

Nguyen, Duyen T., David Hoksza, Patrick May, et al.. (2024). Genomics 2 Proteins portal: a resource and discovery tool for linking genetic screening outputs to protein sequences and structures. Nature Methods. 21(10). 1947–1957. 5 indexed citations

Claussnitzer, Melina, Victoria N. Parikh, Alex H. Wagner, et al.. (2024). Minimum information and guidelines for reporting a multiplexed assay of variant effect. Genome biology. 25(1). 100–100. 8 indexed citations

Bhattacharjee, Pushpak, Miha Pakusch, Eleonora Tresoldi, et al.. (2024). Proinsulin C-peptide is a major source of HLA-DQ8 restricted hybrid insulin peptides recognized by human islet-infiltrating CD4+ T cells. PNAS Nexus. 3(11). 2 indexed citations

Mangiola, Stefano, Alexandra J. Roth‐Schulze, Marie Trussart, et al.. (2023). sccomp: Robust differential composition and variability analysis for single-cell data. Proceedings of the National Academy of Sciences. 120(33). e2203828120–e2203828120. 13 indexed citations

Bedő, Justin, et al.. (2022). Integrating deep mutational scanning and low-throughput mutagenesis data to predict the impact of amino acid variants. GigaScience. 12. 3 indexed citations

Kuang, Da, Jochen Weile, Nishka Kishore, et al.. (2021). MaveRegistry: a collaboration platform for multiplexed assays of variant effect. Bioinformatics. 37(19). 3382–3383. 16 indexed citations

10.

Trenker, Raphael, et al.. (2021). Human and viral membrane–associated E3 ubiquitin ligases MARCH1 and MIR2 recognize different features of CD86 to downregulate surface expression. Journal of Biological Chemistry. 297(1). 100900–100900. 9 indexed citations

11.

Mannering, Stuart I., et al.. (2021). Identifying New Hybrid Insulin Peptides (HIPs) in Type 1 Diabetes. Frontiers in Immunology. 12. 667870–667870. 11 indexed citations

12.

Fayer, Shawn, Carolyn Horton, Jennifer N. Dines, et al.. (2021). Closing the gap: Systematic integration of multiplexed functional data resolves variants of uncertain significance in BRCA1, TP53, and PTEN. The American Journal of Human Genetics. 108(12). 2248–2258. 53 indexed citations

13.

Stearns, Frank W., et al.. (2020). Collateral fitness effects of mutations. Proceedings of the National Academy of Sciences. 117(21). 11597–11607. 30 indexed citations

14.

Bridgford, Jessica L., Su Min Lee, Paola Guglielmelli, et al.. (2019). Novel drivers and modifiers of MPL-dependent oncogenic transformation identified by deep mutational scanning. Blood. 135(4). 287–292. 34 indexed citations

15.

Weile, Jochen, Jay Shendure, Lea M. Starita, et al.. (2019). MaveDB: an open-source platform to distribute and interpret data from multiplexed assays of variant effect. Genome biology. 20(1). 223–223. 132 indexed citations

16.

Gelman, Hannah, Jennifer N. Dines, Jonathan S. Berg, et al.. (2019). Recommendations for the collection and use of multiplexed functional data for clinical variant interpretation. Genome Medicine. 11(1). 85–85. 48 indexed citations

17.

Rubin, Alan F., Hannah Gelman, Nathan Lucas, et al.. (2017). A statistical framework for analyzing deep mutational scanning data. Genome biology. 18(1). 150–150. 139 indexed citations

18.

MacLean, Charles D., Dana Walrath, Alan F. Rubin, et al.. (2004). Adapting Root Cause Analysis to Chronic Medical Conditions. PubMed. 30(4). 175–186. 11 indexed citations

19.

Rubin, Alan F.. (1992). Design of an expert system and its application to dermatopathology. Histopathology. 21(3). 269–274. 6 indexed citations

20.

Goodman, Robert L., et al.. (1986). Psycholigical factors in the choice of treatment for breast cancer. International Journal of Radiation Oncology*Biology*Physics. 12. 151–151. 2 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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