Matthew Lyon

2.2k total citations
14 papers, 567 citations indexed

About

Matthew Lyon is a scholar working on Molecular Biology, Genetics and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Matthew Lyon has authored 14 papers receiving a total of 567 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 5 papers in Genetics and 2 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Matthew Lyon's work include Genomics and Rare Diseases (3 papers), Genetic Associations and Epidemiology (2 papers) and Machine Learning in Healthcare (2 papers). Matthew Lyon is often cited by papers focused on Genomics and Rare Diseases (3 papers), Genetic Associations and Epidemiology (2 papers) and Machine Learning in Healthcare (2 papers). Matthew Lyon collaborates with scholars based in United Kingdom, United States and Germany. Matthew Lyon's co-authors include Tom R. Gaunt, David J. Bunyan, Eleanor G. Seaby, Gibran Hemani, Benjamin Elsworth, Christine Gast, Nikki Graham, Gopalakrishnan Venkat‐Raman, Reuben J. Pengelly and Sarah Ennis and has published in prestigious journals such as Bioinformatics, Genome biology and Journal of Thoracic and Cardiovascular Surgery.

In The Last Decade

Matthew Lyon

13 papers receiving 563 citations

Peers

Matthew Lyon
Matthew Lyon
Citations per year, relative to Matthew Lyon Matthew Lyon (= 1×) peers Shigeo Kamitsuji

Countries citing papers authored by Matthew Lyon

Since Specialization
Citations

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

Fields of papers citing papers by Matthew Lyon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matthew Lyon

This figure shows the co-authorship network connecting the top 25 collaborators of Matthew Lyon. A scholar is included among the top collaborators of Matthew Lyon 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 Matthew Lyon. Matthew Lyon 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.
Lyon, Matthew, Shea J. Andrews, Benjamin Elsworth, et al.. (2021). The variant call format provides efficient and robust storage of GWAS summary statistics. Genome biology. 22(1). 32–32. 118 indexed citations
2.
Staley, James R, Frank Windmeijer, Matthew Suderman, et al.. (2021). A robust mean and variance test with application to high-dimensional phenotypes. European Journal of Epidemiology. 37(4). 377–387. 8 indexed citations
3.
Hemani, Gibran, et al.. (2021). MRCIEU/TwoSampleMR: Various minor updates. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
4.
Wai, Htoo A., Jenny Lord, Matthew Lyon, et al.. (2020). Blood RNA analysis can increase clinical diagnostic rate and resolve variants of uncertain significance. Genetics in Medicine. 22(6). 1005–1014. 103 indexed citations
5.
Liu, Yi, Benjamin Elsworth, Pau Erola, et al.. (2020). EpiGraphDB: a database and data mining platform for health data science. Bioinformatics. 37(9). 1304–1311. 28 indexed citations
6.
Benedetto, Umberto, Arnaldo Dimagli, Shubhra Sinha, et al.. (2020). Machine learning improves mortality risk prediction after cardiac surgery: Systematic review and meta-analysis. Journal of Thoracic and Cardiovascular Surgery. 163(6). 2075–2087.e9. 65 indexed citations
7.
Benedetto, Umberto, Shubhra Sinha, Matthew Lyon, et al.. (2020). Can machine learning improve mortality prediction following cardiac surgery?. European Journal of Cardio-Thoracic Surgery. 58(6). 1130–1136. 21 indexed citations
8.
Lyon, Matthew, et al.. (2020). gwas-vcf-performance. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
9.
Hemani, Gibran, et al.. (2019). MRCIEU/TwoSampleMR: WellcomeOpen. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
10.
Gast, Christine, Reuben J. Pengelly, Matthew Lyon, et al.. (2015). Collagen (COL4A) mutations are the most frequent mutations underlying adult focal segmental glomerulosclerosis. Nephrology Dialysis Transplantation. 31(6). 961–970. 165 indexed citations
11.
Jones, Amy V., Daniel Ward, Matthew Lyon, et al.. (2014). Evaluation of methods to detect CALR mutations in myeloproliferative neoplasms. Leukemia Research. 39(1). 82–87. 46 indexed citations
12.
Lin, Feng, Matthew Lyon, Michela Raponi, et al.. (2011). Systematic screening of FBN1 gene unclassified missense variants for splice abnormalities. Clinical Genetics. 82(3). 223–231. 9 indexed citations
13.
Lyon, Matthew, et al.. (1975). Specific locus mutation rates after repeated small radiation doses to mouse oocytes. Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis. 30. 375–381.
14.
Lyon, Matthew. (1975). Specific locus mutation rates after repeated small radiation doses to mouse oocytes. Mutation Research/Genetic Toxicology. 30(3). 375–381. 1 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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