Neil MacLean

3.3k total citations
38 papers, 816 citations indexed

About

Neil MacLean is a scholar working on Molecular Biology, Hematology and Oncology. According to data from OpenAlex, Neil MacLean has authored 38 papers receiving a total of 816 indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Molecular Biology, 19 papers in Hematology and 5 papers in Oncology. Recurrent topics in Neil MacLean's work include Acute Myeloid Leukemia Research (17 papers), Protein Degradation and Inhibitors (11 papers) and Ubiquitin and proteasome pathways (11 papers). Neil MacLean is often cited by papers focused on Acute Myeloid Leukemia Research (17 papers), Protein Degradation and Inhibitors (11 papers) and Ubiquitin and proteasome pathways (11 papers). Neil MacLean collaborates with scholars based in Canada, United States and Italy. Neil MacLean's co-authors include Aaron D. Schimmer, Rose Hurren, Marcela Gronda, Mark D. Minden, Alessandro Datti, Troy Ketela, Jason Moffat, Mahadeo A. Sukhai, Xinliang Mao and Xiaoming Wang and has published in prestigious journals such as Blood, PLoS ONE and Cancer Research.

In The Last Decade

Neil MacLean

35 papers receiving 808 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Neil MacLean Canada 17 588 253 167 155 92 38 816
Shengyan Xiang United States 16 765 1.3× 273 1.1× 134 0.8× 109 0.7× 58 0.6× 25 919
Isabella Pallavicini Italy 16 715 1.2× 273 1.1× 232 1.4× 291 1.9× 112 1.2× 20 1.0k
Luis A. Carvajal United States 10 550 0.9× 354 1.4× 92 0.6× 134 0.9× 120 1.3× 29 814
Grace R. Anderson United States 11 573 1.0× 190 0.8× 123 0.7× 210 1.4× 54 0.6× 13 763
Andrea Ghelli Luserna di Rorà Italy 13 513 0.9× 303 1.2× 115 0.7× 98 0.6× 76 0.8× 38 735
Ana Slipicevic Norway 15 623 1.1× 311 1.2× 61 0.4× 152 1.0× 87 0.9× 37 823
Miriam Canavese United States 10 674 1.1× 398 1.6× 168 1.0× 62 0.4× 125 1.4× 21 923
Julia Kirshner United States 15 368 0.6× 379 1.5× 175 1.0× 97 0.6× 77 0.8× 30 848
Víctor J. Sánchez‐Arévalo Lobo Spain 14 693 1.2× 311 1.2× 65 0.4× 152 1.0× 56 0.6× 23 887
A M Martelli Italy 10 580 1.0× 191 0.8× 198 1.2× 91 0.6× 89 1.0× 17 785

Countries citing papers authored by Neil MacLean

Since Specialization
Citations

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

Fields of papers citing papers by Neil MacLean

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Neil MacLean

This figure shows the co-authorship network connecting the top 25 collaborators of Neil MacLean. A scholar is included among the top collaborators of Neil MacLean 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 Neil MacLean. Neil MacLean 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.
Barghout, Samir H., Asma M. Aman, Kazem Nouri, et al.. (2021). A genome-wide CRISPR/Cas9 screen in acute myeloid leukemia cells identifies regulators of TAK-243 sensitivity. JCI Insight. 6(5). 24 indexed citations
2.
Lee, Jong Bok, Dilshad H. Khan, Rose Hurren, et al.. (2021). Venetoclax enhances T cell–mediated antileukemic activity by increasing ROS production. Blood. 138(3). 234–245. 103 indexed citations
3.
Schimmer, Aaron D., et al.. (2020). Transduction of Primary AML Cells with Lentiviral Vector for In Vitro Study or In Vivo Engraftment. STAR Protocols. 1(3). 100163–100163. 1 indexed citations
4.
Egan, Grace, Geethu Emily Thomas, Parasvi S. Patel, et al.. (2020). Abstract 3784: The metabolic enzyme Hexokinase 2 localizes to the nucleus and regulates stemness in AML through a kinase independent mechanism. Cancer Research. 80(16_Supplement). 3784–3784. 1 indexed citations
5.
Coyaud, Étienne, Victor S. Nirmalanandhan, Marcela Gronda, et al.. (2019). Global Interactome Mapping of Mitochondrial Intermembrane Space Proteases Identifies a Novel Function for HTRA2. PROTEOMICS. 19(24). e1900139–e1900139. 20 indexed citations
6.
Barghout, Samir H., Neil MacLean, G. Wei Xu, et al.. (2018). A Genome-Wide CRISPR/Cas9 Knockout Screen Identifies BEND3 As a Determinant of Sensitivity to UBA1 Inhibition in Acute Myeloid Leukemia. Blood. 132(Supplement 1). 1350–1350. 1 indexed citations
7.
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9.
Barghout, Samir H., Parasvi S. Patel, Xiaoming Wang, et al.. (2017). TAK-243 Is a Selective UBA1 Inhibitor That Displays Preclinical Activity in Acute Myeloid Leukemia (AML). Blood. 130(Suppl_1). 814–814.
10.
Coyaud, Étienne, Estelle Laurent, Rose Hurren, et al.. (2016). Characterizing the mitochondrial DNA polymerase gamma interactome by BioID identifies Ruvbl2 localizes to the mitochondria. Mitochondrion. 32. 31–35. 13 indexed citations
11.
Gebbia, Marinella, Swayam Prabha, Marcela Gronda, et al.. (2015). Select microtubule inhibitors increase lysosome acidity and promote lysosomal disruption in acute myeloid leukemia (AML) cells. APOPTOSIS. 20(7). 948–959. 18 indexed citations
12.
Wu, Yan, Rose Hurren, Neil MacLean, et al.. (2015). Carnitine transporter CT2 (SLC22A16) is over-expressed in acute myeloid leukemia (AML) and target knockdown reduces growth and viability of AML cells. APOPTOSIS. 20(8). 1099–1108. 40 indexed citations
13.
Jeyaraju, Danny V., Rose Hurren, Xiaoming Wang, et al.. (2015). A Novel Isoflavone, ME-344, Targets the Cytoskeleton in Acute Myeloid Leukemia. Blood. 126(23). 3682–3682. 1 indexed citations
14.
Xu, G. Wei, Julia I. Toth, Stacey-Lynn Paiva, et al.. (2014). Mutations in UBA3 Confer Resistance to the NEDD8-Activating Enzyme Inhibitor MLN4924 in Human Leukemic Cells. PLoS ONE. 9(4). e93530–e93530. 33 indexed citations
15.
Spagnuolo, Paul A., Rose Hurren, Marcela Gronda, et al.. (2013). Inhibition of intracellular dipeptidyl peptidases 8 and 9 enhances parthenolide’s anti-leukemic activity. Leukemia. 27(6). 1236–1244. 46 indexed citations
16.
Simpson, Craig D., Rose Hurren, Dahlia Kasimer, et al.. (2012). A genome wide shRNA screen identifies α/β hydrolase domain containing 4 (ABHD4) as a novel regulator of anoikis resistance. APOPTOSIS. 17(7). 666–678. 19 indexed citations
17.
Zavareh, Reza Beheshti, Mahadeo A. Sukhai, Rose Hurren, et al.. (2012). Suppression of Cancer Progression by MGAT1 shRNA Knockdown. PLoS ONE. 7(9). e43721–e43721. 34 indexed citations
18.
Cole, Alicia M., Zezhou Wang, Rachel Mattson, et al.. (2012). Targeting the Mammalian Mitochondrial Clpp (mClpP) As a Novel Therapeutic Strategy for Acute Myeloid Leukemia. Blood. 120(21). 3603–3603. 1 indexed citations
19.
Eberhard, Yanina, Marcela Gronda, Rose Hurren, et al.. (2011). Inhibition of SREBP1 sensitizes cells to death ligands. Oncotarget. 2(3). 186–196. 27 indexed citations
20.
Wood, Tabitha E., Shadi Dalili, Craig D. Simpson, et al.. (2010). Selective Inhibition of Histone Deacetylases Sensitizes Malignant Cells to Death Receptor Ligands. Molecular Cancer Therapeutics. 9(1). 246–256. 56 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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