Andrew C. Peet

7.0k total citations
142 papers, 3.2k citations indexed

About

Andrew C. Peet is a scholar working on Radiology, Nuclear Medicine and Imaging, Genetics and Molecular Biology. According to data from OpenAlex, Andrew C. Peet has authored 142 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 69 papers in Radiology, Nuclear Medicine and Imaging, 52 papers in Genetics and 42 papers in Molecular Biology. Recurrent topics in Andrew C. Peet's work include Glioma Diagnosis and Treatment (52 papers), Advanced MRI Techniques and Applications (51 papers) and Metabolomics and Mass Spectrometry Studies (17 papers). Andrew C. Peet is often cited by papers focused on Glioma Diagnosis and Treatment (52 papers), Advanced MRI Techniques and Applications (51 papers) and Metabolomics and Mass Spectrometry Studies (17 papers). Andrew C. Peet collaborates with scholars based in United Kingdom, United States and China. Andrew C. Peet's co-authors include Martin Wilson, Theodoros N. Arvanitis, Weitao Yang, Nigel P. Davies, Richard G. Grundy, Risto A. Kauppinen, Greg Reynolds, Lesley MacPherson, Jan Novák and K. Natarajan and has published in prestigious journals such as Nature Communications, The Journal of Chemical Physics and PLoS ONE.

In The Last Decade

Andrew C. Peet

137 papers receiving 3.1k citations

Peers

Andrew C. Peet
Carles Arús Spain
Martin Wilson United Kingdom
Ovidiu C. Andronesi United States
Peter Bachert Germany
Stephan Gruber Austria
Lester Kwock United States
M. Schumacher Germany
Jeffrey L. Evelhoch United States
David M. Silver United States
Marinette van der Graaf Netherlands
Carles Arús Spain
Andrew C. Peet
Citations per year, relative to Andrew C. Peet Andrew C. Peet (= 1×) peers Carles Arús

Countries citing papers authored by Andrew C. Peet

Since Specialization
Citations

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

Fields of papers citing papers by Andrew C. Peet

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Andrew C. Peet. 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 Andrew C. Peet. The network helps show where Andrew C. Peet may publish in the future.

Co-authorship network of co-authors of Andrew C. Peet

This figure shows the co-authorship network connecting the top 25 collaborators of Andrew C. Peet. A scholar is included among the top collaborators of Andrew C. Peet 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 Andrew C. Peet. Andrew C. Peet 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.
Grist, James T., Dorothee P. Auer, Shivaram Avula, et al.. (2024). Noise suppression of proton magnetic resonance spectroscopy improves paediatric brain tumour classification. NMR in Biomedicine. 37(6). e5129–e5129.
2.
Lindsey, Janet C., Alaide Morcavallo, Florence Burté, et al.. (2024). MYC-dependent upregulation of the de novo serine and glycine synthesis pathway is a targetable metabolic vulnerability in group 3 medulloblastoma. Neuro-Oncology. 27(1). 237–253. 3 indexed citations
3.
Grist, James T., Nigel P. Davies, Martin Wilson, et al.. (2022). Metabolite selection for machine learning in childhood brain tumour classification. NMR in Biomedicine. 35(6). e4673–e4673. 11 indexed citations
4.
Badodi, Sara, Nicola Pomella, Xinyu Zhang, et al.. (2021). Inositol treatment inhibits medulloblastoma through suppression of epigenetic-driven metabolic adaptation. Nature Communications. 12(1). 2148–2148. 19 indexed citations
5.
Avula, Shivaram, Andrew C. Peet, Giovanni Morana, et al.. (2021). Correction to: European Society for Paediatric Oncology (SIOPE) MRI guidelines for imaging patients with central nervous system tumours. Child s Nervous System. 37(8). 2509–2510. 3 indexed citations
6.
Davies, Nigel P., K. Natarajan, Laurence Abernethy, et al.. (2021). Added value of magnetic resonance spectroscopy for diagnosing childhood cerebellar tumours. NMR in Biomedicine. 35(2). e4630–e4630. 4 indexed citations
7.
Lourdusamy, Anbarasu, Donald Macarthur, Andrew C. Peet, et al.. (2021). Meta‐Analysis of Apparent Diffusion Coefficient in Pediatric Medulloblastoma, Ependymoma, and Pilocytic Astrocytoma. Journal of Magnetic Resonance Imaging. 56(1). 147–157. 10 indexed citations
8.
Avula, Shivaram, Andrew C. Peet, Giovanni Morana, et al.. (2021). European Society for Paediatric Oncology (SIOPE) MRI guidelines for imaging patients with central nervous system tumours. Child s Nervous System. 37(8). 2497–2508. 35 indexed citations
9.
Nadaf, Javad, Leanne de Kock, Anne‐Sophie Chong, et al.. (2021). Molecular characterization of DICER1-mutated pituitary blastoma. Acta Neuropathologica. 141(6). 929–944. 17 indexed citations
10.
Gill, Simrandip K., Martin Wilson, Nigel P. Davies, et al.. (2019). Ex vivo metabolite profiling of paediatric central nervous system tumours reveals prognostic markers. Scientific Reports. 9(1). 10473–10473. 68 indexed citations
11.
Carlin, Dominic, et al.. (2018). Short-acquisition-time JPRESS and its application to paediatric brain tumours. Magnetic Resonance Materials in Physics Biology and Medicine. 32(2). 247–258. 1 indexed citations
12.
Carlin, Dominic, et al.. (2018). Variation of T2 relaxation times in pediatric brain tumors and their effect on metabolite quantification. Journal of Magnetic Resonance Imaging. 49(1). 195–203. 6 indexed citations
13.
14.
Zarinabad, Niloufar, et al.. (2018). Automated Modular Magnetic Resonance Imaging Clinical Decision Support System (MIROR): An Application in Pediatric Cancer Diagnosis. JMIR Medical Informatics. 6(2). e30–e30. 5 indexed citations
15.
Webb, Emma, Dominic Carlin, Martin Wilson, et al.. (2017). Quantitative Brain MRI in Congenital Adrenal Hyperplasia: In Vivo Assessment of the Cognitive and Structural Impact of Steroid Hormones. The Journal of Clinical Endocrinology & Metabolism. 103(4). 1330–1341. 31 indexed citations
16.
Wilson, Martin, Simrandip K. Gill, Lesley MacPherson, et al.. (2014). Noninvasive Detection of Glutamate Predicts Survival in Pediatric Medulloblastoma. Clinical Cancer Research. 20(17). 4532–4539. 34 indexed citations
17.
Pan, Xiaoyan, Martin Wilson, Carmel McConville, et al.. (2012). The lipid composition of isolated cytoplasmic lipid droplets from a human cancer cell line, BE(2)M17. Molecular BioSystems. 8(6). 1694–1700. 5 indexed citations
18.
Wilson, Martin, Carole Cummins, Lesley MacPherson, et al.. (2012). Magnetic resonance spectroscopy metabolite profiles predict survival in paediatric brain tumours. European Journal of Cancer. 49(2). 457–464. 46 indexed citations
19.
Pan, Xiaoyan, Martin Wilson, Carmel McConville, et al.. (2012). Increased unsaturation of lipids in cytoplasmic lipid droplets in DAOY cancer cells in response to cisplatin treatment. Metabolomics. 9(3). 722–729. 33 indexed citations
20.
Rahman, Ruman, Teresa Osteso-Ibáñez, Robert A. Hirst, et al.. (2010). Histone Deacetylase Inhibition Attenuates Cell Growth with Associated Telomerase Inhibition in High-Grade Childhood Brain Tumor Cells. Molecular Cancer Therapeutics. 9(9). 2568–2581. 31 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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