William J. Astle

7.5k total citations
14 papers, 807 citations indexed

About

William J. Astle is a scholar working on Molecular Biology, Hematology and Genetics. According to data from OpenAlex, William J. Astle has authored 14 papers receiving a total of 807 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 5 papers in Hematology and 4 papers in Genetics. Recurrent topics in William J. Astle's work include Blood groups and transfusion (4 papers), Metabolomics and Mass Spectrometry Studies (3 papers) and Platelet Disorders and Treatments (3 papers). William J. Astle is often cited by papers focused on Blood groups and transfusion (4 papers), Metabolomics and Mass Spectrometry Studies (3 papers) and Platelet Disorders and Treatments (3 papers). William J. Astle collaborates with scholars based in United Kingdom, Canada and Australia. William J. Astle's co-authors include David J. Balding, Maria De Iorio, Timothy M. D. Ebbels, Jie Hao, Manuel Liebeke, Jacob G. Bundy, Ernest Turro, Simon Tavaré, Marina Cecelja and Mario Falchi and has published in prestigious journals such as Journal of the American Statistical Association, Blood and Bioinformatics.

In The Last Decade

William J. Astle

13 papers receiving 794 citations

Peers

William J. Astle
Gonçalo Graça United Kingdom
Anders Pedersen Sweden
Stanislav O. Zakharkin United States
Claire Teague United Kingdom
Danny Simpson United Kingdom
Chunyu Ma United States
Thomas N. Plasterer United States
Arthur Moseley United States
Lingling Wang China
Michele Costanzo Italy
Gonçalo Graça United Kingdom
William J. Astle
Citations per year, relative to William J. Astle William J. Astle (= 1×) peers Gonçalo Graça

Countries citing papers authored by William J. Astle

Since Specialization
Citations

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

Fields of papers citing papers by William J. Astle

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of William J. Astle

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

All Works

14 of 14 papers shown
1.
2.
Thomas, Patrick, Joana Batista, Carly Kempster, et al.. (2023). A signature of platelet reactivity in CBC scattergrams reveals genetic predictors of thrombotic disease risk. Blood. 142(22). 1895–1908. 5 indexed citations
3.
Collins, Janine, William J. Astle, Karyn Mégy, Andrew Mumford, & Dragana Vuckovic. (2021). Advances in understanding the pathogenesis of hereditary macrothrombocytopenia. British Journal of Haematology. 195(1). 25–45. 7 indexed citations
4.
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Wood, Angela, Stephen Kaptoge, Adam S. Butterworth, et al.. (2018). Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies. Research Portal (Queen's University Belfast). 55 indexed citations
6.
Zou, Siying, Myrto Kostadima, William J. Astle, et al.. (2017). SNP in human ARHGEF3 promoter is associated with DNase hypersensitivity, transcript level and platelet function, and Arhgef3 KO mice have increased mean platelet volume. PLoS ONE. 12(5). e0178095–e0178095. 13 indexed citations
7.
Pardo, Mercedes, Janine Collins, Tadbir K. Bariana, et al.. (2017). Nbeal2 interacts with Dock7, Sec16a, and Vac14. Blood. 131(9). 1000–1011. 23 indexed citations
8.
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Hao, Jie, Manuel Liebeke, William J. Astle, et al.. (2014). Bayesian deconvolution and quantification of metabolites in complex 1D NMR spectra using BATMAN. Nature Protocols. 9(6). 1416–1427. 144 indexed citations
10.
Direk, Kenan, Marina Cecelja, William J. Astle, et al.. (2013). The relationship between DXA-based and anthropometric measures of visceral fat and morbidity in women. BMC Cardiovascular Disorders. 13(1). 25–25. 58 indexed citations
11.
Turro, Ernest, William J. Astle, & Simon Tavaré. (2013). Flexible analysis of RNA-seq data using mixed effects models. Bioinformatics. 30(2). 180–188. 40 indexed citations
12.
Astle, William J., Maria De Iorio, Sylvia Richardson, David A. Stephens, & Timothy M. D. Ebbels. (2012). A Bayesian Model of NMR Spectra for the Deconvolution and Quantification of Metabolites in Complex Biological Mixtures. Journal of the American Statistical Association. 107(500). 1259–1271. 34 indexed citations
13.
Hao, Jie, William J. Astle, Maria De Iorio, & Timothy M. D. Ebbels. (2012). BATMAN—an R package for the automated quantification of metabolites from nuclear magnetic resonance spectra using a Bayesian model. Bioinformatics. 28(15). 2088–2090. 113 indexed citations
14.
Astle, William J. & David J. Balding. (2009). Population Structure and Cryptic Relatedness in Genetic Association Studies. Statistical Science. 24(4). 306 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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