Chris J. Novotny

1.2k total citations
10 papers, 829 citations indexed

About

Chris J. Novotny is a scholar working on Molecular Biology, Oncology and Nutrition and Dietetics. According to data from OpenAlex, Chris J. Novotny has authored 10 papers receiving a total of 829 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 4 papers in Oncology and 2 papers in Nutrition and Dietetics. Recurrent topics in Chris J. Novotny's work include Cell death mechanisms and regulation (4 papers), PI3K/AKT/mTOR signaling in cancer (4 papers) and Trace Elements in Health (2 papers). Chris J. Novotny is often cited by papers focused on Cell death mechanisms and regulation (4 papers), PI3K/AKT/mTOR signaling in cancer (4 papers) and Trace Elements in Health (2 papers). Chris J. Novotny collaborates with scholars based in United States, United Kingdom and Australia. Chris J. Novotny's co-authors include Kevan M. Shokat, Danny Hsu, Paul J. Hergenrother, Masanori Okaniwa, Quinn P. Peterson, Wai Wong, Sabina Cosulich, Helen Won, Neal Rosen and Vanessa Rodrik-Outmezguine and has published in prestigious journals such as Nature, Cancer Cell and Cancer Research.

In The Last Decade

Chris J. Novotny

9 papers receiving 823 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chris J. Novotny United States 9 642 179 131 82 81 10 829
Nanni Huser United States 8 737 1.1× 238 1.3× 79 0.6× 84 1.0× 73 0.9× 15 999
Martin Pass United Kingdom 16 551 0.9× 250 1.4× 212 1.6× 81 1.0× 69 0.9× 31 873
Vladimir Cmiljanović Switzerland 6 614 1.0× 120 0.7× 142 1.1× 56 0.7× 92 1.1× 15 794
Vishal Pendharkar Singapore 12 714 1.1× 270 1.5× 137 1.0× 67 0.8× 56 0.7× 15 1.0k
Kurt G. Pike United Kingdom 16 633 1.0× 131 0.7× 210 1.6× 66 0.8× 59 0.7× 37 863
Nicole Streiner United States 9 1.1k 1.8× 312 1.7× 159 1.2× 105 1.3× 114 1.4× 16 1.4k
Christian Borgo Italy 18 702 1.1× 224 1.3× 56 0.4× 70 0.9× 53 0.7× 38 991
Mike I. Walton United Kingdom 7 630 1.0× 283 1.6× 73 0.6× 118 1.4× 112 1.4× 9 856
Kevin Hudson United Kingdom 17 779 1.2× 223 1.2× 132 1.0× 156 1.9× 76 0.9× 30 1.1k
Michael P. Sheets United States 13 584 0.9× 152 0.8× 161 1.2× 74 0.9× 153 1.9× 22 967

Countries citing papers authored by Chris J. Novotny

Since Specialization
Citations

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

Fields of papers citing papers by Chris J. Novotny

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chris J. Novotny. 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 Chris J. Novotny. The network helps show where Chris J. Novotny may publish in the future.

Co-authorship network of co-authors of Chris J. Novotny

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

All Works

10 of 10 papers shown
1.
Yang, Guang, Deanne Francis, James R. Krycer, et al.. (2021). Dissecting the biology of mTORC1 beyond rapamycin. Science Signaling. 14(701). eabe0161–eabe0161. 19 indexed citations
2.
Fan, Qi-Wen, Ozlem Aksoy, Robyn A. Wong, et al.. (2017). A Kinase Inhibitor Targeted to mTORC1 Drives Regression in Glioblastoma. Cancer Cell. 31(3). 424–435. 145 indexed citations
3.
Novotny, Chris J., Gregory L. Hamilton, Frank McCormick, & Kevan M. Shokat. (2017). Farnesyltransferase-Mediated Delivery of a Covalent Inhibitor Overcomes Alternative Prenylation to Mislocalize K-Ras. ACS Chemical Biology. 12(7). 1956–1962. 33 indexed citations
4.
Novotny, Chris J., Sirkku Pollari, Jin H. Park, et al.. (2016). Overcoming resistance to HER2 inhibitors through state-specific kinase binding. Nature Chemical Biology. 12(11). 923–930. 28 indexed citations
5.
Rodrik-Outmezguine, Vanessa, Masanori Okaniwa, Zhan Yao, et al.. (2016). Overcoming mTOR resistance mutations with a new-generation mTOR inhibitor. Nature. 534(7606). 272–276. 341 indexed citations
6.
Rodrik-Outmezguine, Vanessa, Masanori Okaniwa, Zhan Yao, et al.. (2016). Abstract 2147: Overcoming mTOR resistance mutations with a new generation mTOR inhibitor. Cancer Research. 76(14_Supplement). 2147–2147.
7.
Hsu, Danny, Howard S. Roth, Diana C. West, et al.. (2011). Parallel Synthesis and Biological Evaluation of 837 Analogues of Procaspase-Activating Compound 1 (PAC-1). ACS Combinatorial Science. 14(1). 44–50. 38 indexed citations
8.
Peterson, Quinn P., Danny Hsu, Chris J. Novotny, et al.. (2010). Discovery and Canine Preclinical Assessment of a Nontoxic Procaspase-3–Activating Compound. Cancer Research. 70(18). 7232–7241. 51 indexed citations
9.
Schmit, Joanna, Quinn P. Peterson, Diana C. West, et al.. (2010). Pharmacokinetics and derivation of an anticancer dosing regimen for PAC-1, a preferential small molecule activator of procaspase-3, in healthy dogs. Investigational New Drugs. 29(5). 901–911. 39 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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