Chris Riffkin

1.7k total citations
10 papers, 326 citations indexed

About

Chris Riffkin is a scholar working on Molecular Biology, Cancer Research and Genetics. According to data from OpenAlex, Chris Riffkin has authored 10 papers receiving a total of 326 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 3 papers in Cancer Research and 2 papers in Genetics. Recurrent topics in Chris Riffkin's work include Cell death mechanisms and regulation (2 papers), DNA Repair Mechanisms (2 papers) and Multiple Myeloma Research and Treatments (2 papers). Chris Riffkin is often cited by papers focused on Cell death mechanisms and regulation (2 papers), DNA Repair Mechanisms (2 papers) and Multiple Myeloma Research and Treatments (2 papers). Chris Riffkin collaborates with scholars based in Australia, China and United States. Chris Riffkin's co-authors include David C.S. Huang, Michael Foley, Leann Tilley, Alan R. Hibbs, David Segal, Guillaume Lessène, Andrew W. Roberts, Yuan Yao, Marco J. Herold and Seong Lin Khaw and has published in prestigious journals such as Nature Communications, Blood and British Journal of Cancer.

In The Last Decade

Chris Riffkin

10 papers receiving 324 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 Riffkin Australia 7 213 81 65 65 62 10 326
Irene Riz United States 10 257 1.2× 78 1.0× 123 1.9× 37 0.6× 31 0.5× 13 315
Takashi Ikeda Japan 12 270 1.3× 61 0.8× 36 0.6× 62 1.0× 29 0.5× 19 452
Sharon Shechter United States 11 264 1.2× 63 0.8× 24 0.4× 20 0.3× 44 0.7× 22 372
Chiel Maas Netherlands 13 342 1.6× 104 1.3× 50 0.8× 91 1.4× 14 0.2× 14 444
Claire Harris United Kingdom 9 354 1.7× 131 1.6× 19 0.3× 51 0.8× 23 0.4× 15 442
Sunmi Han South Korea 6 419 2.0× 47 0.6× 49 0.8× 218 3.4× 64 1.0× 15 617
Michelle L. Churchman United States 12 426 2.0× 105 1.3× 121 1.9× 63 1.0× 149 2.4× 24 738
Karin B. Kindle United Kingdom 14 401 1.9× 124 1.5× 65 1.0× 53 0.8× 22 0.4× 16 552
J Robert-Lézénès France 12 245 1.2× 67 0.8× 71 1.1× 69 1.1× 13 0.2× 22 348
Sanil Bhatia Germany 13 351 1.6× 201 2.5× 93 1.4× 112 1.7× 29 0.5× 40 534

Countries citing papers authored by Chris Riffkin

Since Specialization
Citations

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

Fields of papers citing papers by Chris Riffkin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chris Riffkin

This figure shows the co-authorship network connecting the top 25 collaborators of Chris Riffkin. A scholar is included among the top collaborators of Chris Riffkin 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 Riffkin. Chris Riffkin 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.
Lew, Thomas E., Mark F. van Delft, Chris Riffkin, et al.. (2023). Depletion of the Mitochondrial E3 Ligase MARCH5 Induces Synthetic Lethality to BCL2 Inhibitor (Venetoclax) Therapy in Cell Lines Representative of Diverse Blood Cancers. Blood. 142(Supplement 1). 415–415. 1 indexed citations
2.
Luo, Mingjie, Michelle Palmieri, Chris Riffkin, et al.. (2020). Defining the susceptibility of colorectal cancers to BH3-mimetic compounds. Cell Death and Disease. 11(9). 735–735. 13 indexed citations
3.
Masamsetti, V. Pragathi, Ronnie Ren Jie Low, Ka Sin Mak, et al.. (2019). Replication stress induces mitotic death through parallel pathways regulated by WAPL and telomere deprotection. Nature Communications. 10(1). 4224–4224. 33 indexed citations
4.
Khong, Tiffany, David Segal, Yuan Yao, et al.. (2016). Hierarchy for targeting prosurvival BCL2 family proteins in multiple myeloma: pivotal role of MCL1. Blood. 128(14). 1834–1844. 121 indexed citations
5.
Yao, Yuan, Jianan Gong, David Segal, et al.. (2016). The Role of BAX/BAK-Mediated Apoptosis for the Cytotoxic Action of Anti-Myeloma Agents. Blood. 128(22). 5706–5706. 2 indexed citations
6.
Jabbour, Anissa M., Jarrod J. Sandow, Chris Riffkin, et al.. (2013). Akt1 is the principal Akt isoform regulating apoptosis in limiting cytokine concentrations. Cell Death and Differentiation. 20(10). 1341–1349. 42 indexed citations
7.
Sandow, Jarrod J., Anissa M. Jabbour, Mark R. Condina, et al.. (2011). Cytokine receptor signaling activates an IKK-dependent phosphorylation of PUMA to prevent cell death. Cell Death and Differentiation. 19(4). 633–641. 26 indexed citations
8.
Ashley, David M., Chris Riffkin, Thomas Mikeska, et al.. (2008). In vitro sensitivity testing of minimally passaged and uncultured gliomas with TRAIL and/or chemotherapy drugs. British Journal of Cancer. 99(2). 294–304. 17 indexed citations
9.
Maddox, J F, Chris Riffkin, & K.J. Beh. (2000). Dinucleotide repeat polymorphism at the ovine Mcma1, Mcma2, Mcma5, Mcma8, Mcma9, Mcma11, Mcma14, Mcma20, Mcma24, Mcma26 loci. Animal Genetics. 31(2). 148–148. 4 indexed citations
10.

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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