Mathew J. Garnett

39.3k total citations · 6 hit papers
85 papers, 9.8k citations indexed

About

Mathew J. Garnett is a scholar working on Molecular Biology, Oncology and Cancer Research. According to data from OpenAlex, Mathew J. Garnett has authored 85 papers receiving a total of 9.8k indexed citations (citations by other indexed papers that have themselves been cited), including 55 papers in Molecular Biology, 26 papers in Oncology and 23 papers in Cancer Research. Recurrent topics in Mathew J. Garnett's work include Cancer Genomics and Diagnostics (21 papers), CRISPR and Genetic Engineering (13 papers) and Computational Drug Discovery Methods (13 papers). Mathew J. Garnett is often cited by papers focused on Cancer Genomics and Diagnostics (21 papers), CRISPR and Genetic Engineering (13 papers) and Computational Drug Discovery Methods (13 papers). Mathew J. Garnett collaborates with scholars based in United Kingdom, United States and Germany. Mathew J. Garnett's co-authors include Richard Marais, Ultan McDermott, David Barford, Cyril H. Benes, Howard Lightfoot, Dan Niculescu‐Duvaz, Valerie M. Good, S. Mark Roe, Chris M. Jones and Caroline J. Springer and has published in prestigious journals such as Nature, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Mathew J. Garnett

83 papers receiving 9.7k citations

Hit Papers

Genomics of Drug Sensitivity in Cancer (GDSC): a resour... 2004 2026 2011 2018 2012 2004 2004 2017 2022 500 1000 1.5k 2.0k 2.5k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells
    2012 2.6k citations Wanjuan Yang, Jorge Soares et al. Nucleic Acids Research profile →
  2. Mechanism of Activation of the RAF-ERK Signaling Pathway by Oncogenic Mutations of B-RAF
    2004 2.2k citations Mathew J. Garnett et al. profile →
  3. Guilty as charged
    2004 636 citations Mathew J. Garnett et al. profile →
  4. Perturbation-response genes reveal signaling footprints in cancer gene expression
    2017 444 citations Bertram Klinger, Anja Sieber et al. Nature Communications profile →
  5. High-content CRISPR screening
    2022 276 citations Mathew J. Garnett et al. profile →
  6. High-content CRISPR screening
    2022 170 citations Mathew J. Garnett et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mathew J. Garnett United Kingdom 38 7.1k 3.1k 1.9k 1.7k 1.5k 85 9.8k
Cyril H. Benes United States 49 7.4k 1.0× 4.0k 1.3× 2.5k 1.3× 2.9k 1.7× 906 0.6× 123 10.9k
Ultan McDermott United Kingdom 39 8.2k 1.2× 4.8k 1.6× 3.6k 1.9× 3.9k 2.2× 1.1k 0.7× 94 13.0k
Kevin B. Kim United States 38 5.4k 0.8× 5.1k 1.7× 970 0.5× 1.5k 0.9× 854 0.6× 116 8.2k
Michael A. Davies United States 57 7.4k 1.0× 6.9k 2.2× 1.9k 1.0× 2.6k 1.5× 661 0.4× 325 12.3k
Udai Banerji United Kingdom 40 4.3k 0.6× 2.7k 0.9× 1.2k 0.6× 1.2k 0.7× 564 0.4× 207 7.4k
Roger S. Lo United States 41 10.5k 1.5× 6.4k 2.1× 2.0k 1.0× 1.1k 0.7× 1.1k 0.7× 70 14.2k
Hubing Shi China 33 5.5k 0.8× 3.4k 1.1× 1.2k 0.6× 632 0.4× 797 0.5× 83 7.5k
Sreenath V. Sharma United States 31 5.4k 0.8× 4.1k 1.3× 1.7k 0.9× 3.2k 1.8× 387 0.3× 39 8.8k
Paul D. Smith United Kingdom 45 5.1k 0.7× 2.4k 0.8× 1.4k 0.7× 1.4k 0.8× 320 0.2× 120 7.5k
Keiran S.M. Smalley United States 53 5.9k 0.8× 4.0k 1.3× 837 0.4× 715 0.4× 750 0.5× 173 8.5k

Countries citing papers authored by Mathew J. Garnett

Since Specialization
Citations

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

Fields of papers citing papers by Mathew J. Garnett

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mathew J. Garnett

This figure shows the co-authorship network connecting the top 25 collaborators of Mathew J. Garnett. A scholar is included among the top collaborators of Mathew J. Garnett 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 Mathew J. Garnett. Mathew J. Garnett 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.
Burgold, Thomas, Emre Karakoç, Emanuel Gonçalves, et al.. (2025). A next-generation dual guide CRISPR system for genetic interaction library screening. Nature Communications. 17(1). 561–561. 1 indexed citations
2.
Chai, Annie Wai Yeeng, et al.. (2024). High TNF and NF-κB Pathway Dependency Are Associated with AZD5582 Sensitivity in OSCC via CASP8-Dependent Apoptosis. Cancer Research Communications. 4(11). 2919–2932.
3.
Manipur, Ichcha, Clare Pacini, Fiona M. Behan, et al.. (2024). Benchmark Software and Data for Evaluating CRISPR-Cas9 Experimental Pipelines Through the Assessment of a Calibration Screen. The CRISPR Journal. 7(6). 355–365. 1 indexed citations
4.
Chai, Annie Wai Yeeng, Aks Chiang, Larry Ka-Yue Chow, et al.. (2024). Establishment and Characterization of an Epstein-Barr Virus–positive Cell Line from a Non-keratinizing Differentiated Primary Nasopharyngeal Carcinoma. Cancer Research Communications. 4(3). 645–659. 7 indexed citations
5.
Cai, Zhaoxiang, Clare Pacini, Susana Vinga, et al.. (2024). Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning. Nature Communications. 15(1). 10390–10390. 11 indexed citations
6.
Dorard, Coralie, Olivier Buhard, Quentin Letourneur, et al.. (2023). RAF1 contributes to cell proliferation and STAT3 activation in colorectal cancer independently of microsatellite and KRAS status. Oncogene. 42(20). 1649–1660. 12 indexed citations
7.
Schipper, Luuk J., Laurien J. Zeverijn, Mathew J. Garnett, & Emile E. Voest. (2022). Can Drug Repurposing Accelerate Precision Oncology?. Cancer Discovery. 12(7). 1634–1641. 24 indexed citations
8.
Pacini, Clare, Joshua M. Dempster, Isabella Boyle, et al.. (2021). Integrated cross-study datasets of genetic dependencies in cancer. Nature Communications. 12(1). 1661–1661. 145 indexed citations
9.
Eduati, Federica, Patricia Jaaks, Thorsten Cramer, et al.. (2020). Patient‐specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies. Molecular Systems Biology. 16(2). e8664–e8664. 48 indexed citations
10.
Eduati, Federica, Patricia Jaaks, Thorsten Cramer, et al.. (2020). Patient‐specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies. Molecular Systems Biology. 16(6). 24 indexed citations
11.
Francies, Hayley E., Ultan McDermott, & Mathew J. Garnett. (2020). Genomics-guided pre-clinical development of cancer therapies. Nature Cancer. 1(5). 482–492. 22 indexed citations
12.
Dastur, Anahita, Carlotta Costa, August Williams, et al.. (2018). NOTCH1 Represses MCL-1 Levels in GSI-resistant T-ALL, Making them Susceptible to ABT-263. Clinical Cancer Research. 25(1). 312–324. 16 indexed citations
13.
Buczacki, Simon J. A., Emma K. Biggs, Chrysa Koukorava, et al.. (2018). Itraconazole targets cell cycle heterogeneity in colorectal cancer. The Journal of Experimental Medicine. 215(7). 1891–1912. 53 indexed citations
14.
Cokelaer, Thomas, Elisabeth Chen, Francesco Iorio, et al.. (2017). GDSCTools for mining pharmacogenomic interactions in cancer. Bioinformatics. 34(7). 1226–1228. 41 indexed citations
15.
Eduati, Federica, Bertram Klinger, Thomas Cokelaer, et al.. (2017). Drug Resistance Mechanisms in Colorectal Cancer Dissected with Cell Type–Specific Dynamic Logic Models. Cancer Research. 77(12). 3364–3375. 73 indexed citations
16.
García‐Alonso, Luz, Francesco Iorio, Angela Matchan, et al.. (2017). Transcription Factor Activities Enhance Markers of Drug Sensitivity in Cancer. Cancer Research. 78(3). 769–780. 107 indexed citations
17.
Gill, Sonja J., Jon Travers, Syd Barthorpe, et al.. (2015). Combinations of PARP Inhibitors with Temozolomide Drive PARP1 Trapping and Apoptosis in Ewing’s Sarcoma. PLoS ONE. 10(10). e0140988–e0140988. 62 indexed citations
18.
He, Lei, Kristine Torres‐Lockhart, Nicole Forster, et al.. (2012). Mcl-1 and FBW7 Control a Dominant Survival Pathway Underlying HDAC and Bcl-2 Inhibitor Synergy in Squamous Cell Carcinoma. Cancer Discovery. 3(3). 324–337. 54 indexed citations
19.
Yang, Wanjuan, Jorge Soares, Patricia Greninger, et al.. (2012). Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Research. 41(D1). D955–D961. 2597 indexed citations breakdown →
20.
Emuss, Victoria, et al.. (2005). Mutations of C-RAF Are Rare in Human Cancer because C-RAF Has a Low Basal Kinase Activity Compared with B-RAF. Cancer Research. 65(21). 9719–9726. 143 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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