David Segal

4.0k total citations
38 papers, 1.9k citations indexed

About

David Segal is a scholar working on Molecular Biology, Hematology and Oncology. According to data from OpenAlex, David Segal has authored 38 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Molecular Biology, 12 papers in Hematology and 11 papers in Oncology. Recurrent topics in David Segal's work include Cell death mechanisms and regulation (8 papers), Chronic Lymphocytic Leukemia Research (6 papers) and Protein Degradation and Inhibitors (6 papers). David Segal is often cited by papers focused on Cell death mechanisms and regulation (8 papers), Chronic Lymphocytic Leukemia Research (6 papers) and Protein Degradation and Inhibitors (6 papers). David Segal collaborates with scholars based in Australia, United States and China. David Segal's co-authors include Victoria C. Foletta, Donna Cohén, David C.S. Huang, Andrew W. Roberts, Marco J. Herold, Meir Wetzler, Herbert C. Morse, Andreas Strasser, Guillaume Lessène and David Westerman and has published in prestigious journals such as The Journal of Experimental Medicine, SHILAP Revista de lepidopterología and Blood.

In The Last Decade

David Segal

38 papers receiving 1.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David Segal Australia 21 1.0k 467 464 429 316 38 1.9k
Florian Grebien Austria 25 1.3k 1.2× 793 1.7× 323 0.7× 450 1.0× 448 1.4× 62 2.4k
Christopher L. Reading United States 19 731 0.7× 448 1.0× 350 0.8× 338 0.8× 218 0.7× 74 1.8k
Jeremy A. Ross United States 22 1.2k 1.2× 871 1.9× 406 0.9× 622 1.4× 310 1.0× 91 2.0k
Arnaud Jacquel France 33 1.7k 1.6× 583 1.2× 718 1.5× 514 1.2× 323 1.0× 64 3.0k
Yaël Zermati France 18 1.6k 1.5× 427 0.9× 687 1.5× 282 0.7× 347 1.1× 24 2.6k
Melanie J. McConnell New Zealand 25 968 0.9× 207 0.4× 314 0.7× 245 0.6× 169 0.5× 49 1.6k
Esteban S. Masuda United States 33 1.2k 1.2× 187 0.4× 1.2k 2.5× 384 0.9× 206 0.7× 78 2.6k
Gaël Roué Spain 33 1.7k 1.7× 385 0.8× 552 1.2× 864 2.0× 568 1.8× 101 3.0k
Runqing Lu United States 23 759 0.7× 193 0.4× 797 1.7× 444 1.0× 206 0.7× 36 2.0k
Didier Grillot United States 26 1.5k 1.5× 286 0.6× 976 2.1× 634 1.5× 192 0.6× 39 2.7k

Countries citing papers authored by David Segal

Since Specialization
Citations

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

Fields of papers citing papers by David Segal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Segal

This figure shows the co-authorship network connecting the top 25 collaborators of David Segal. A scholar is included among the top collaborators of David Segal 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 Segal. David Segal 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.
Segal, David, et al.. (2024). Adjustment experiences of adolescents living with well-controlled type 1 diabetes using closed-loop technology. SHILAP Revista de lepidopterología. 5. 1445972–1445972. 1 indexed citations
2.
3.
Park, Jiyoung, Kwangjun Lee, Clarisse G. Ricci, et al.. (2023). PERIOD phosphorylation leads to feedback inhibition of CK1 activity to control circadian period. Molecular Cell. 83(10). 1677–1692.e8. 23 indexed citations
4.
Teh, Charis E., Jianan Gong, David Segal, et al.. (2020). Deep profiling of apoptotic pathways with mass cytometry identifies a synergistic drug combination for killing myeloma cells. Cell Death and Differentiation. 27(7). 2217–2233. 25 indexed citations
5.
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
6.
Lee, Erinna F., Tiffany J. Harris, Sharon Tran, et al.. (2019). BCL-XL and MCL-1 are the key BCL-2 family proteins in melanoma cell survival. Cell Death and Disease. 10(5). 342–342. 142 indexed citations
7.
Doerflinger, Marcel, Christina Nedeva, James C. Paton, et al.. (2017). DR5 and caspase-8 are dispensable in ER stress-induced apoptosis. Cell Death and Differentiation. 24(5). 944–950. 60 indexed citations
8.
Tanzer, Maria C., Nufail Khan, James Rickard, et al.. (2017). Combination of IAP antagonist and IFNγ activates novel caspase-10- and RIPK1-dependent cell death pathways. Cell Death and Differentiation. 24(3). 481–491. 38 indexed citations
9.
Antignani, Antonella, David Segal, Nathan C. Simon, et al.. (2017). Essential role for Bim in mediating the apoptotic and antitumor activities of immunotoxins. Oncogene. 36(35). 4953–4962. 11 indexed citations
10.
Moujalled, Donia M., David Segal, Giovanna Pomilio, et al.. (2017). Enhancing venetoclax activity in acute myeloid leukemia by co-targeting MCL1. Leukemia. 32(2). 303–312. 124 indexed citations
11.
Carrington, Emma M., Yifan Zhan, Jamie L. Brady, et al.. (2017). Anti-apoptotic proteins BCL-2, MCL-1 and A1 summate collectively to maintain survival of immune cell populations both in vitro and in vivo. Cell Death and Differentiation. 24(5). 878–888. 116 indexed citations
12.
Anderson, Mary Ann, Jing Deng, John F. Seymour, et al.. (2016). The BCL2 selective inhibitor venetoclax induces rapid onset apoptosis of CLL cells in patients via a TP53-independent mechanism. Blood. 127(25). 3215–3224. 215 indexed citations
13.
Xu, Zhen, Phillip P. Sharp, Yuan Yao, et al.. (2016). BET inhibition represses miR17-92 to drive BIM-initiated apoptosis of normal and transformed hematopoietic cells. Leukemia. 30(7). 1531–1541. 25 indexed citations
14.
Burns, Christopher J., David Segal, & Andrew F. Wilks. (2012). The Use of JAK-Specific Inhibitors as Chemical Biology Tools. Methods in molecular biology. 967. 99–113. 2 indexed citations
15.
Burns, Christopher J., Helmut Hügel, David C.S. Huang, et al.. (2012). Synthesis and biological evaluation of a potent salicylihalamide A lactam analogue. Organic & Biomolecular Chemistry. 10(40). 8147–8147. 8 indexed citations
16.
17.
Foletta, Victoria C., Matthew J. Prior, Nicole Stupka, et al.. (2009). NDRG2, a novel regulator of myoblast proliferation, is regulated by anabolic and catabolic factors. The Journal of Physiology. 587(7). 1619–1634. 49 indexed citations
18.
Carey, Kate A., David Segal, Reuben Klein, et al.. (2006). Identification of novel genes expressed during rhabdomyosarcoma differentiation using cDNA microarrays. Pathology International. 56(5). 246–255. 15 indexed citations
19.
Segal, David, Carin Lassen, Urban Gullberg, et al.. (2004). Identification of genes differentially regulated by the P210 BCR/ABL1 fusion oncogene using cDNA microarrays. Experimental Hematology. 32(5). 476–482. 26 indexed citations
20.
Walder, Ken, David Segal, Guy Augert, et al.. (2002). A Custom‐Built Insulin Resistance Gene Chip. Annals of the New York Academy of Sciences. 967(1). 274–282. 4 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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