John Mayer

462 total citations
17 papers, 286 citations indexed

About

John Mayer is a scholar working on Molecular Biology, Genetics and Artificial Intelligence. According to data from OpenAlex, John Mayer has authored 17 papers receiving a total of 286 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 9 papers in Genetics and 3 papers in Artificial Intelligence. Recurrent topics in John Mayer's work include Genetic Associations and Epidemiology (7 papers), Genomics and Rare Diseases (4 papers) and Machine Learning in Healthcare (3 papers). John Mayer is often cited by papers focused on Genetic Associations and Epidemiology (7 papers), Genomics and Rare Diseases (4 papers) and Machine Learning in Healthcare (3 papers). John Mayer collaborates with scholars based in United States, Australia and Sweden. John Mayer's co-authors include Zhan Ye, Scott J. Hebbring, David Page, Peggy Peissig, Eric LaRose, Murray H. Brilliant, Majid Rastegar-Mojarad, Simon Lin, Jason H. Karnes and Christian M. Shaffer and has published in prestigious journals such as Nature Communications, Bioinformatics and Scientific Reports.

In The Last Decade

John Mayer

16 papers receiving 285 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
John Mayer United States	9	103	99	60	39	29	17	286
Vivek A. Rudrapatna United States	11	151 1.5×	61 0.6×	43 0.7×	81 2.1×	18 0.6×	33	449
Sarah Sandmann Germany	11	198 1.9×	90 0.9×	79 1.3×	12 0.3×	49 1.7×	40	584
Loren L. Armstrong United States	7	168 1.6×	109 1.1×	66 1.1×	10 0.3×	48 1.7×	9	389
Travis Osterman United States	11	119 1.2×	106 1.1×	70 1.2×	19 0.5×	40 1.4×	33	504
Arturo López Pineda United States	8	82 0.8×	34 0.3×	129 2.1×	9 0.2×	43 1.5×	16	366
Iain S. Forrest United States	10	78 0.8×	90 0.9×	21 0.3×	14 0.4×	33 1.1×	22	327
Dania Alkhafaji Saudi Arabia	9	52 0.5×	60 0.6×	34 0.6×	17 0.4×	35 1.2×	15	354
Abiel Roche-Lima Puerto Rico	9	86 0.8×	23 0.2×	32 0.5×	15 0.4×	22 0.8×	46	369
Noah Weston United States	3	145 1.4×	91 0.9×	115 1.9×	4 0.1×	89 3.1×	4	342
Ben Omega Petrazzini United States	8	53 0.5×	67 0.7×	28 0.5×	9 0.2×	33 1.1×	18	268

Countries citing papers authored by John Mayer

Since Specialization

Citations

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

Fields of papers citing papers by John Mayer

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of John Mayer

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

All Works

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17 of 17 papers shown

Sathipati, Srinivasulu Yerukala, Sohyun Jeong, Param Sharma, et al.. (2024). Exploring prognostic implications of miRNA signatures and telomere maintenance genes in kidney cancer. PubMed. 32(4). 200874–200874.

Ye, Zhan, John Mayer, Terrie Kitchner, et al.. (2023). Estimating the efficacy of pharmacogenomics over a lifetime. Frontiers in Medicine. 10. 1006743–1006743. 4 indexed citations

Valenzuela, Robert K., Terrie Kitchner, John Mayer, et al.. (2023). Genetic risk score in multiple sclerosis is associated with unique gut microbiome. Scientific Reports. 13(1). 16269–16269. 6 indexed citations

Patterson, Brian W., Michael S. Pulia, John Mayer, et al.. (2022). Multisite evaluation of prediction models for emergency department crowding before and during the COVID-19 pandemic. Journal of the American Medical Informatics Association. 30(2). 292–300. 5 indexed citations

Tatonetti, Nicholas P., et al.. (2021). E-Pedigrees: a large-scale automatic family pedigree prediction application. Bioinformatics. 37(21). 3966–3968. 2 indexed citations

Kim, Dae‐Hee, et al.. (2021). Medical Big Data Analysis System to Discover Associations between Genetic Variants and Diseases. 1 indexed citations

LaRose, Eric, et al.. (2019). Machine learning for phenotyping opioid overdose events. Journal of Biomedical Informatics. 94. 103185–103185. 30 indexed citations

Johnson, Michael G., et al.. (2019). The −839(A/C) Polymorphism in the ECE1 Isoform b Promoter Associates With Osteoporosis and Fractures. Journal of the Endocrine Society. 3(11). 2041–2050. 1 indexed citations

Karnes, Jason H., Lisa Bastarache, Christian M. Shaffer, et al.. (2017). Phenome-wide scanning identifies multiple diseases and disease severity phenotypes associated with HLA variants. Science Translational Medicine. 9(389). 74 indexed citations

10.

Elston, Robert C., Guilherme J. M. Rosa, John Mayer, et al.. (2017). Applying family analyses to electronic health records to facilitate genetic research. Bioinformatics. 34(4). 635–642. 6 indexed citations

11.

Tafti, Ahmad P., Eric LaRose, John Mayer, et al.. (2017). Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure. JMIR Medical Informatics. 5(4). e51–e51. 32 indexed citations

12.

Tafti, Ahmad P., Mehdi Assefi, Eric LaRose, et al.. (2017). bigNN: An open-source big data toolkit focused on biomedical sentence classification. 3888–3896. 13 indexed citations

13.

Mosley, Jonathan D., John S. Witte, Emma K. Larkin, et al.. (2016). Identifying genetically driven clinical phenotypes using linear mixed models. Nature Communications. 7(1). 11433–11433. 8 indexed citations

14.

Liu, Jixia, Zhan Ye, John Mayer, et al.. (2016). Phenome-wide association study maps new diseases to the human major histocompatibility complex region. Journal of Medical Genetics. 53(10). 681–689. 27 indexed citations

15.

Hebbring, Scott J., et al.. (2015). Application of clinical text data for phenome-wide association studies (PheWASs). Bioinformatics. 31(12). 1981–1987. 33 indexed citations

16.

Ye, Zhan, John Mayer, Lynn Ivacic, et al.. (2014). Phenome-wide association studies (PheWASs) for functional variants. European Journal of Human Genetics. 23(4). 523–529. 35 indexed citations

17.

Mayer, John, Terrie Kitchner, Zhan Ye, et al.. (2014). Use of an Electronic Medical Record to Create the Marshfield Clinic Twin/Multiple Birth Cohort. Genetic Epidemiology. 38(8). 692–698. 9 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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