Graeme Benstead-Hume

649 total citations
12 papers, 341 citations indexed

About

Graeme Benstead-Hume is a scholar working on Molecular Biology, Computational Theory and Mathematics and Cancer Research. According to data from OpenAlex, Graeme Benstead-Hume has authored 12 papers receiving a total of 341 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 5 papers in Computational Theory and Mathematics and 5 papers in Cancer Research. Recurrent topics in Graeme Benstead-Hume's work include Bioinformatics and Genomic Networks (7 papers), Computational Drug Discovery Methods (5 papers) and Cancer Genomics and Diagnostics (4 papers). Graeme Benstead-Hume is often cited by papers focused on Bioinformatics and Genomic Networks (7 papers), Computational Drug Discovery Methods (5 papers) and Cancer Genomics and Diagnostics (4 papers). Graeme Benstead-Hume collaborates with scholars based in United Kingdom and United States. Graeme Benstead-Hume's co-authors include Frances M. G. Pearl, Jessica A. Downs, Penny A. Jeggo, Cornelia Meisenberg, Christopher Richardson, Thomas R. Simon, Barbara Koch, Georgios Giamas, Giles Critchley and Nicolas A. Stewart and has published in prestigious journals such as Nature Communications, Molecular Cell and International Journal of Molecular Sciences.

In The Last Decade

Graeme Benstead-Hume

12 papers receiving 338 citations

Peers

Graeme Benstead-Hume
Yubo Fan United States
Meghan M. Joly United States
Huikun Zhang China
Jon A. Oyer United States
Rui S. R. Machado Portugal
Anja Bastian United States
Claudio Laquatra Italy
Francisco Campos‐Laborie Spain
Yubo Fan United States
Graeme Benstead-Hume
Citations per year, relative to Graeme Benstead-Hume Graeme Benstead-Hume (= 1×) peers Yubo Fan

Countries citing papers authored by Graeme Benstead-Hume

Since Specialization
Citations

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

Fields of papers citing papers by Graeme Benstead-Hume

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Graeme Benstead-Hume. 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 Graeme Benstead-Hume. The network helps show where Graeme Benstead-Hume may publish in the future.

Co-authorship network of co-authors of Graeme Benstead-Hume

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

All Works

12 of 12 papers shown
1.
Benstead-Hume, Graeme, et al.. (2022). Biological network topology features predict gene dependencies in cancer cell-lines. Bioinformatics Advances. 2(1). vbac084–vbac084. 4 indexed citations
2.
Zuazua‐Villar, Pedro, Theodoros I. Roumeliotis, Graeme Benstead-Hume, et al.. (2022). Aneuploidy tolerance caused by BRG1 loss allows chromosome gains and recovery of fitness. Nature Communications. 13(1). 1731–1731. 11 indexed citations
3.
Benstead-Hume, Graeme, et al.. (2019). Predicting synthetic lethal interactions using conserved patterns in protein interaction networks. PLoS Computational Biology. 15(4). e1006888–e1006888. 30 indexed citations
4.
Simon, Thomas R., Barbara Koch, Nicolas A. Stewart, et al.. (2019). Cell-derived extracellular vesicles can be used as a biomarker reservoir for glioblastoma tumor subtyping. Communications Biology. 2(1). 315–315. 77 indexed citations
5.
Benstead-Hume, Graeme, et al.. (2019). Defining Signatures of Arm-Wise Copy Number Change and Their Associated Drivers in Kidney Cancers. International Journal of Molecular Sciences. 20(22). 5762–5762. 8 indexed citations
6.
Meisenberg, Cornelia, et al.. (2018). Repression of Transcription at DNA Breaks Requires Cohesin throughout Interphase and Prevents Genome Instability. Molecular Cell. 73(2). 212–223.e7. 87 indexed citations
7.
Benstead-Hume, Graeme, et al.. (2017). Bioinformatics in translational drug discovery. Bioscience Reports. 37(4). 79 indexed citations
8.
Benstead-Hume, Graeme, et al.. (2017). Identification and analysis of mutational hotspots in oncogenes and tumour suppressors. Oncotarget. 8(13). 21290–21304. 18 indexed citations
9.
Benstead-Hume, Graeme, et al.. (2017). ‘Big data’ approaches for novel anti-cancer drug discovery. Expert Opinion on Drug Discovery. 12(6). 599–609. 7 indexed citations
10.
Benstead-Hume, Graeme, et al.. (2017). Computational Approaches to Identify Genetic Interactions for Cancer Therapeutics. Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics. 14(3). 3 indexed citations
11.
Benstead-Hume, Graeme, et al.. (2016). Mutational patterns in oncogenes and tumour suppressors. Biochemical Society Transactions. 44(3). 925–931. 13 indexed citations
12.
Cantarel, Brandi L., Yunping Lei, Daniel Weaver, et al.. (2015). Analysis of archived residual newborn screening blood spots after whole genome amplification. BMC Genomics. 16(1). 602–602. 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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