David P. Toczyski

5.6k total citations · 2 hit papers
48 papers, 3.7k citations indexed

About

David P. Toczyski is a scholar working on Molecular Biology, Cell Biology and Oncology. According to data from OpenAlex, David P. Toczyski has authored 48 papers receiving a total of 3.7k indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Molecular Biology, 20 papers in Cell Biology and 11 papers in Oncology. Recurrent topics in David P. Toczyski's work include DNA Repair Mechanisms (21 papers), Microtubule and mitosis dynamics (15 papers) and Ubiquitin and proteasome pathways (15 papers). David P. Toczyski is often cited by papers focused on DNA Repair Mechanisms (21 papers), Microtubule and mitosis dynamics (15 papers) and Ubiquitin and proteasome pathways (15 papers). David P. Toczyski collaborates with scholars based in United States, Switzerland and Canada. David P. Toczyski's co-authors include Justine A. Melo, Leland H. Hartwell, Amanda G. Paulovich, Nancy L. Maas, Carla Y. Bonilla, Kyle M. Miller, Joan A. Steitz, Stephanie Cheung, Genevieve M. Vidanes and Jennifer A. Benanti and has published in prestigious journals such as Nature, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

David P. Toczyski

47 papers receiving 3.7k citations

Hit Papers

When Checkpoints Fail 1997 2026 2006 2016 1997 1997 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David P. Toczyski United States 28 3.4k 1.2k 829 460 447 48 3.7k
Nick Rhind United States 34 3.8k 1.1× 1.3k 1.0× 610 0.7× 405 0.9× 483 1.1× 69 4.2k
Noel F. Lowndes Ireland 36 4.6k 1.4× 787 0.6× 1.1k 1.3× 835 1.8× 419 0.9× 83 5.1k
Zhanyun Tang United States 28 4.3k 1.3× 1.8k 1.4× 612 0.7× 320 0.7× 531 1.2× 33 4.9k
Clare H. McGowan United States 35 4.6k 1.4× 1.6k 1.3× 1.6k 1.9× 612 1.3× 450 1.0× 47 5.1k
Sang Eun Lee United States 31 5.0k 1.5× 642 0.5× 1.0k 1.2× 811 1.8× 746 1.7× 56 5.3k
Helfrid Hochegger United Kingdom 31 2.8k 0.8× 991 0.8× 1.1k 1.4× 365 0.8× 459 1.0× 45 3.3k
Nancy C. Walworth United States 23 3.7k 1.1× 2.3k 1.9× 817 1.0× 290 0.6× 295 0.7× 36 4.4k
Dana Branzei Italy 38 5.0k 1.5× 1.0k 0.8× 986 1.2× 965 2.1× 535 1.2× 84 5.3k
Hideo Nishitani Japan 34 4.2k 1.2× 1.5k 1.2× 1.0k 1.2× 279 0.6× 274 0.6× 79 4.7k
Arne Lindqvist Sweden 27 2.4k 0.7× 1.5k 1.2× 855 1.0× 240 0.5× 200 0.4× 49 2.9k

Countries citing papers authored by David P. Toczyski

Since Specialization
Citations

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

Fields of papers citing papers by David P. Toczyski

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David P. Toczyski

This figure shows the co-authorship network connecting the top 25 collaborators of David P. Toczyski. A scholar is included among the top collaborators of David P. Toczyski 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 P. Toczyski. David P. Toczyski 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.
Suresh, Harsha Garadi, Henry N. Ward, Ian Jones, et al.. (2024). The PRC2.1 subcomplex opposes G1 progression through regulation of CCND1 and CCND2. eLife. 13.
2.
Tian, Ruilin, et al.. (2021). A comprehensive phenotypic CRISPR-Cas9 screen of the ubiquitin pathway uncovers roles of ubiquitin ligases in mitosis. Molecular Cell. 81(6). 1319–1336.e9. 27 indexed citations
3.
Biggins, Sue, et al.. (2020). Fifty years of cycling. Molecular Biology of the Cell. 31(26). 2868–2870. 1 indexed citations
4.
Forné, Ignasi, Matías Capella, Marco Simonetta, et al.. (2018). Shelterin and subtelomeric DNA sequences control nucleosome maintenance and genome stability. EMBO Reports. 20(1). 29 indexed citations
5.
Lao, Jessica P., Jeffrey R. Johnson, Billy W. Newton, et al.. (2018). The Yeast DNA Damage Checkpoint Kinase Rad53 Targets the Exoribonuclease, Xrn1. G3 Genes Genomes Genetics. 8(12). 3931–3944. 16 indexed citations
6.
Topacio, Benjamin R., et al.. (2015). DNA Damage Regulates Translation through β-TRCP Targeting of CReP. PLoS Genetics. 11(6). e1005292–e1005292. 29 indexed citations
7.
Downey, Michael, Jeffrey R. Johnson, Norman E. Davey, et al.. (2014). Acetylome Profiling Reveals Overlap in the Regulation of Diverse Processes by Sirtuins, Gcn5, and Esa1. Molecular & Cellular Proteomics. 14(1). 162–176. 51 indexed citations
8.
Mark, Kevin G., Marco Simonetta, Alessio Maiolica, Charles A. Seller, & David P. Toczyski. (2014). Ubiquitin Ligase Trapping Identifies an SCFSaf1 Pathway Targeting Unprocessed Vacuolar/Lysosomal Proteins. Molecular Cell. 53(1). 148–161. 41 indexed citations
9.
Toczyski, David P., et al.. (2012). Colocalization of Mec1 and Mrc1 is sufficient for Rad53 phosphorylation in vivo. Molecular Biology of the Cell. 23(6). 1058–1067. 21 indexed citations
10.
Benanti, Jennifer A., Mary E. Matyskiela, David O. Morgan, & David P. Toczyski. (2009). Functionally Distinct Isoforms of Cik1 Are Differentially Regulated by APC/C-Mediated Proteolysis. Molecular Cell. 33(5). 581–590. 26 indexed citations
11.
Vega, Leticia R., et al.. (2007). Sensitivity of Yeast Strains with Long G-Tails to Levels of Telomere-Bound Telomerase. PLoS Genetics. 3(6). e105–e105. 39 indexed citations
12.
Benanti, Jennifer A., et al.. (2007). A proteomic screen reveals SCFGrr1 targets that regulate the glycolytic–gluconeogenic switch. Nature Cell Biology. 9(10). 1184–1191. 75 indexed citations
13.
Toczyski, David P.. (2006). Methods for Studying Adaptation to the DNA Damage Checkpoint in Yeast. Methods in enzymology on CD-ROM/Methods in enzymology. 409. 150–165. 4 indexed citations
14.
Miller, Kyle M., Nancy L. Maas, & David P. Toczyski. (2006). Taking It Off: Regulation of H3 K56 Acetylation by Hst3 and Hst4. Cell Cycle. 5(22). 2561–2565. 28 indexed citations
15.
Maas, Nancy L., et al.. (2006). Cell Cycle and Checkpoint Regulation of Histone H3 K56 Acetylation by Hst3 and Hst4. Molecular Cell. 23(1). 109–119. 193 indexed citations
16.
Garber, Peter M., Genevieve M. Vidanes, & David P. Toczyski. (2005). Damage in transition. Trends in Biochemical Sciences. 30(2). 63–66. 12 indexed citations
17.
Kaye, Julia, Justine A. Melo, Stephanie Cheung, et al.. (2004). DNA Breaks Promote Genomic Instability by Impeding Proper Chromosome Segregation. Current Biology. 14(23). 2096–2106. 129 indexed citations
18.
Toczyski, David P., et al.. (2003). Securin and B-cyclin/CDK are the only essential targets of the APC. Nature Cell Biology. 5(12). 1090–1094. 146 indexed citations
19.
Melo, Justine A., et al.. (2001). Two checkpoint complexes are independently recruited to sites of DNA damage in vivo. Genes & Development. 15(21). 2809–2821. 307 indexed citations
20.
Toczyski, David P. & Joan A. Steitz. (1993). The Cellular RNA-Binding Protein EAP Recognizes a Conserved Stem-Loop in the Epstein-Barr Virus Small RNA EBER 1. Molecular and Cellular Biology. 13(1). 703–710. 20 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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