Michael L. Mayo

1.3k total citations
54 papers, 558 citations indexed

About

Michael L. Mayo is a scholar working on Molecular Biology, Health, Toxicology and Mutagenesis and Genetics. According to data from OpenAlex, Michael L. Mayo has authored 54 papers receiving a total of 558 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Molecular Biology, 10 papers in Health, Toxicology and Mutagenesis and 9 papers in Genetics. Recurrent topics in Michael L. Mayo's work include Gene Regulatory Network Analysis (19 papers), Bioinformatics and Genomic Networks (10 papers) and Toxic Organic Pollutants Impact (6 papers). Michael L. Mayo is often cited by papers focused on Gene Regulatory Network Analysis (19 papers), Bioinformatics and Genomic Networks (10 papers) and Toxic Organic Pollutants Impact (6 papers). Michael L. Mayo collaborates with scholars based in United States, Türkiye and Iraq. Michael L. Mayo's co-authors include Edward J. Perkins, Preetam Ghosh, Svetlana Kilina, Karen H. Watanabe, Gerald T. Ankley, Daniel L. Villeneuve, Rory B. Conolly, Wan‐Yun Cheng, David Miller and Sergei Tretiak and has published in prestigious journals such as The Journal of Chemical Physics, SHILAP Revista de lepidopterología and Environmental Science & Technology.

In The Last Decade

Michael L. Mayo

49 papers receiving 553 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael L. Mayo United States 13 155 121 89 68 62 54 558
Maria d’Errico Italy 9 139 0.9× 119 1.0× 51 0.6× 16 0.2× 26 0.4× 17 449
Yoshihito Mori Japan 15 88 0.6× 106 0.9× 55 0.6× 60 0.9× 161 2.6× 44 699
Peter Carl Germany 13 155 1.0× 45 0.4× 32 0.4× 129 1.9× 87 1.4× 19 662
Xinxin Zhang China 13 43 0.3× 168 1.4× 30 0.3× 112 1.6× 37 0.6× 36 442
Bashar Ibrahim Germany 23 744 4.8× 38 0.3× 25 0.3× 71 1.0× 44 0.7× 74 1.2k
Jorge Numata Germany 14 264 1.7× 247 2.0× 36 0.4× 27 0.4× 14 0.2× 34 661
Chunyi Liu China 19 190 1.2× 16 0.1× 64 0.7× 12 0.2× 75 1.2× 71 998
Olga Zeni Italy 25 202 1.3× 103 0.9× 58 0.7× 90 1.3× 405 6.5× 67 1.6k

Countries citing papers authored by Michael L. Mayo

Since Specialization
Citations

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

Fields of papers citing papers by Michael L. Mayo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael L. Mayo. 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 Michael L. Mayo. The network helps show where Michael L. Mayo may publish in the future.

Co-authorship network of co-authors of Michael L. Mayo

This figure shows the co-authorship network connecting the top 25 collaborators of Michael L. Mayo. A scholar is included among the top collaborators of Michael L. Mayo 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 Michael L. Mayo. Michael L. Mayo 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.
Russell, Nicholas J., et al.. (2024). The Dynamic Spatial Structure of Flocks. Entropy. 26(3). 234–234.
2.
Mayo, Michael L., et al.. (2024). Entropy-based guidance of deep neural networks for accelerated convergence and improved performance. Information Sciences. 681. 121239–121239. 2 indexed citations
3.
4.
5.
Sweeney, Lisa, et al.. (2022). Toxicokinetic Modeling of Per- and Polyfluoroalkyl Substance Concentrations within Developing Zebrafish (Danio rerio) Populations. Environmental Science & Technology. 56(18). 13189–13199. 15 indexed citations
6.
Rowland, Michael A., et al.. (2021). Devil in the details: Mechanistic variations impact information transfer across models of transcriptional cascades. PLoS ONE. 16(1). e0245094–e0245094. 1 indexed citations
7.
Rowland, Michael A., Todd M. Swannack, Michael L. Mayo, et al.. (2021). COVID-19 infection data encode a dynamic reproduction number in response to policy decisions with secondary wave implications. Scientific Reports. 11(1). 10875–10875. 4 indexed citations
8.
Rowland, Michael A., Andrew M. Hein, Jie Sun, et al.. (2020). Decoding collective communications using information theory tools. Journal of The Royal Society Interface. 17(164). 20190563–20190563. 27 indexed citations
9.
Mayo, Michael L., Jed O. Eberly, Fiona H. Crocker, & Karl J. Indest. (2020). Modeling a synthetic aptamer-based riboswitch biosensor sensitive to low hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) concentrations. PLoS ONE. 15(11). e0241664–e0241664. 2 indexed citations
10.
Eberly, Jed O., et al.. (2019). Detection of hexahydro-1,3-5-trinitro-1,3,5-triazine (RDX) with a microbial sensor. The Journal of General and Applied Microbiology. 65(3). 145–150. 7 indexed citations
11.
Conolly, Rory B., Gerald T. Ankley, Wan‐Yun Cheng, et al.. (2017). Quantitative Adverse Outcome Pathways and Their Application to Predictive Toxicology. Environmental Science & Technology. 51(8). 4661–4672. 162 indexed citations
12.
Watanabe, Karen H., Michael L. Mayo, Kathleen Jensen, et al.. (2016). Predicting Fecundity of Fathead Minnows (Pimephales promelas) Exposed to Endocrine-Disrupting Chemicals Using a MATLAB®-Based Model of Oocyte Growth Dynamics. PLoS ONE. 11(1). e0146594–e0146594. 10 indexed citations
13.
Mayo, Michael L., et al.. (2015). Multiscale Modeling of Information Conveyed by Gene-Regulatory Signaling.. 148–151. 1 indexed citations
14.
Mayo, Michael L., et al.. (2015). Abundance of connected motifs in transcriptional networks, a case study using random forests regression.. 108–115.
15.
Mayo, Michael L., et al.. (2015). Data-Driven Method to Estimate Nonlinear Chemical Equivalence. PLoS ONE. 10(7). e0130494–e0130494. 5 indexed citations
16.
Ghosh, Preetam, et al.. (2015). Transcriptional Network Growing Models Using Motif-Based Preferential Attachment. Frontiers in Bioengineering and Biotechnology. 3. 157–157. 12 indexed citations
17.
Mayo, Michael L., et al.. (2014). Uncertainty in multi-media fate and transport models: A case study for TNT life cycle assessment. The Science of The Total Environment. 494-495. 104–112. 10 indexed citations
18.
Mayo, Michael L., et al.. (2014). Top-level dynamics and the regulated gene response of feed-forward loop transcriptional motifs. Physical Review E. 90(3). 32706–32706. 2 indexed citations
19.
Mayo, Michael L., et al.. (2012). Motif Participation by Genes in E. coli Transcriptional Networks. Frontiers in Physiology. 3. 357–357. 10 indexed citations
20.
Hou, Chen & Michael L. Mayo. (2011). Pulmonary diffusional screening and the scaling laws of mammalian metabolic rates. Physical Review E. 84(6). 61915–61915. 3 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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