Tyler D. Ames

1.8k total citations
38 papers, 1.3k citations indexed

About

Tyler D. Ames is a scholar working on Molecular Biology, Oncology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Tyler D. Ames has authored 38 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Molecular Biology, 14 papers in Oncology and 9 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Tyler D. Ames's work include RNA modifications and cancer (9 papers), RNA and protein synthesis mechanisms (9 papers) and Radiopharmaceutical Chemistry and Applications (5 papers). Tyler D. Ames is often cited by papers focused on RNA modifications and cancer (9 papers), RNA and protein synthesis mechanisms (9 papers) and Radiopharmaceutical Chemistry and Applications (5 papers). Tyler D. Ames collaborates with scholars based in United States, Spain and France. Tyler D. Ames's co-authors include Ronald R. Breaker, Zasha Weinberg, Adam Roth, Jimin Wang, Sarah V. Lipchock, Kathryn D. Smith, Scott A. Strobel, Thomas W. Molitor, Michelle M. Meyer and Derek Hagman and has published in prestigious journals such as Nucleic Acids Research, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.

In The Last Decade

Tyler D. Ames

36 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tyler D. Ames United States 15 936 244 147 145 119 38 1.3k
O. Barábas Germany 21 882 0.9× 248 1.0× 28 0.2× 89 0.6× 107 0.9× 42 1.2k
Kipros Gabriel Australia 19 1.1k 1.2× 183 0.8× 61 0.4× 33 0.2× 89 0.7× 22 1.6k
Mai H. Tran United States 11 574 0.6× 61 0.3× 106 0.7× 72 0.5× 57 0.5× 13 1.2k
Paul J. Romaniuk Canada 30 2.0k 2.2× 273 1.1× 99 0.7× 60 0.4× 199 1.7× 69 2.6k
Lolke de Haan Netherlands 19 455 0.5× 116 0.5× 42 0.3× 108 0.7× 224 1.9× 30 1.2k
Kaixiang Zhu China 15 518 0.6× 137 0.6× 29 0.2× 161 1.1× 273 2.3× 35 1.1k
Minsun Hong South Korea 16 559 0.6× 222 0.9× 49 0.3× 34 0.2× 132 1.1× 39 1.4k
Grégory Boël France 17 823 0.9× 383 1.6× 22 0.1× 59 0.4× 169 1.4× 24 1.2k
Zhemin Zhang China 20 283 0.3× 50 0.2× 194 1.3× 81 0.6× 459 3.9× 86 1.2k
Veronica De Sanctis Italy 14 470 0.5× 60 0.2× 96 0.7× 62 0.4× 286 2.4× 30 1.0k

Countries citing papers authored by Tyler D. Ames

Since Specialization
Citations

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

Fields of papers citing papers by Tyler D. Ames

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tyler D. Ames. 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 Tyler D. Ames. The network helps show where Tyler D. Ames may publish in the future.

Co-authorship network of co-authors of Tyler D. Ames

This figure shows the co-authorship network connecting the top 25 collaborators of Tyler D. Ames. A scholar is included among the top collaborators of Tyler D. Ames 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 Tyler D. Ames. Tyler D. Ames 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.
Sato, Ai, Emma Guilbaud, Christina Y. Yim, et al.. (2025). Partial mitochondrial involvement in the antiproliferative and immunostimulatory effects of PT-112. OncoImmunology. 14(1). 2507245–2507245.
2.
Moreno‐Loshuertos, Raquel, Christina Y. Yim, Tyler D. Ames, et al.. (2024). Cancer cell-selective induction of mitochondrial stress and immunogenic cell death by PT-112 in human prostate cell lines. Journal of Translational Medicine. 22(1). 927–927. 4 indexed citations
3.
Guilbaud, Emma, et al.. (2023). 1106 Molecular mechanisms of immunogenic cell death driven by PT-112. SHILAP Revista de lepidopterología. A1218–A1218. 2 indexed citations
4.
Yim, Christina Y., Marta Martínez‐Júlvez, Raquel Moreno‐Loshuertos, et al.. (2022). PT-112 Induces Mitochondrial Stress and Immunogenic Cell Death, Targeting Tumor Cells with Mitochondrial Deficiencies. Cancers. 14(16). 3851–3851. 7 indexed citations
5.
Karp, Daniel D., D. Ross Camidge, J. Infante, et al.. (2022). Phase I study of PT-112, a novel pyrophosphate-platinum immunogenic cell death inducer, in advanced solid tumours. EClinicalMedicine. 49. 101430–101430. 25 indexed citations
6.
Ames, Tyler D., et al.. (2022). Abstract 1115: PT-112 induces potent mitochondrial stress and immunogenic cell death in human prostate cancer cell lines. Cancer Research. 82(12_Supplement). 1115–1115.
7.
Karp, Daniel D., Roxana Dronca, D. Ross Camidge, et al.. (2020). 1026MO Phase Ib dose escalation study of novel immunogenic cell death (ICD) inducer PT-112 plus PD-L1 inhibitor avelumab in solid tumours. Annals of Oncology. 31. S708–S708. 5 indexed citations
8.
Camidge, D. Ross, et al.. (2018). PT-112: A well-tolerated novel immunogenic cell death (ICD) inducer with activity in advanced solid tumors. Annals of Oncology. 29. viii143–viii143. 11 indexed citations
9.
Weinberg, Zasha, Christina E. Weinberg, Tyler D. Ames, et al.. (2017). Detection of 224 candidate structured RNAs by comparative analysis of specific subsets of intergenic regions. Nucleic Acids Research. 45(18). 10811–10823. 108 indexed citations
10.
Tosi, Diégo, Philippe Pourquier, Tyler D. Ames, et al.. (2017). Abstract 2378: A kinome analysis of the molecular pharmacodynamics of PT-112 in a human cancer cell line. Cancer Research. 77(13_Supplement). 2378–2378. 2 indexed citations
11.
Pinel, Dominic, Štefan Bauer, Amanda L. Muehlbauer, et al.. (2016). Quantitative Trait Loci (QTL)-Guided Metabolic Engineering of a Complex Trait. ACS Synthetic Biology. 6(3). 566–581. 16 indexed citations
12.
Nelson, James W., Mark S. Plummer, Ken Blount, Tyler D. Ames, & Ronald R. Breaker. (2015). Small Molecule Fluoride Toxicity Agonists. Chemistry & Biology. 22(4). 527–534. 24 indexed citations
14.
Roth, Adam, et al.. (2013). A widespread self-cleaving ribozyme class is revealed by bioinformatics. Nature Chemical Biology. 10(1). 56–60. 178 indexed citations
15.
Ames, Tyler D. & Ronald R. Breaker. (2011). Bacterial aptamers that selectively bind glutamine. RNA Biology. 8(1). 82–89. 73 indexed citations
16.
Ames, Tyler D., Dmitry A. Rodionov, Zasha Weinberg, & Ronald R. Breaker. (2010). A Eubacterial Riboswitch Class That Senses the Coenzyme Tetrahydrofolate. Chemistry & Biology. 17(7). 681–685. 78 indexed citations
17.
Smith, Kathryn D., Sarah V. Lipchock, Tyler D. Ames, et al.. (2009). Structural basis of ligand binding by a c-di-GMP riboswitch. Nature Structural & Molecular Biology. 16(12). 1218–1223. 241 indexed citations
18.
Meyer, Michelle M., Tyler D. Ames, Daniel P. Smith, et al.. (2009). Identification of candidate structured RNAs in the marine organism 'Candidatus Pelagibacter ubique'. BMC Genomics. 10(1). 268–268. 56 indexed citations
19.
Meyer, Michelle M., et al.. (2009). A variant riboswitch aptamer class for S-adenosylmethionine common in marine bacteria. RNA. 15(11). 2046–2056. 91 indexed citations
20.
Godden, S., et al.. (2007). Effects of Feeding Heat-Treated Colostrum on Passive Transfer of Immune and Nutritional Parameters in Neonatal Dairy Calves. Journal of Dairy Science. 90(11). 5189–5198. 156 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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