Matthew D. Dyer

2.0k total citations
21 papers, 1.4k citations indexed

About

Matthew D. Dyer is a scholar working on Molecular Biology, Pulmonary and Respiratory Medicine and Virology. According to data from OpenAlex, Matthew D. Dyer has authored 21 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Molecular Biology, 5 papers in Pulmonary and Respiratory Medicine and 3 papers in Virology. Recurrent topics in Matthew D. Dyer's work include Bioinformatics and Genomic Networks (7 papers), Machine Learning in Bioinformatics (6 papers) and Genomics and Phylogenetic Studies (5 papers). Matthew D. Dyer is often cited by papers focused on Bioinformatics and Genomic Networks (7 papers), Machine Learning in Bioinformatics (6 papers) and Genomics and Phylogenetic Studies (5 papers). Matthew D. Dyer collaborates with scholars based in United States, United Kingdom and Netherlands. Matthew D. Dyer's co-authors include T. M. Murali, Bruno Sobral, Tom Slezak, Michael G. Katze, Carol Zhou, Adam Zemła, Jungnam Lee, Jianxin Wang, Kyudong Han and Richard Cordaux and has published in prestigious journals such as Nucleic Acids Research, Journal of Clinical Oncology and Bioinformatics.

In The Last Decade

Matthew D. Dyer

18 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Matthew D. Dyer United States 13 1.0k 218 217 172 158 21 1.4k
David Alderton United Kingdom 8 512 0.5× 75 0.3× 221 1.0× 135 0.8× 107 0.7× 8 918
G. Lynn Law United States 22 926 0.9× 90 0.4× 138 0.6× 98 0.6× 195 1.2× 32 1.3k
Peter Skewes-Cox United States 14 530 0.5× 137 0.6× 343 1.6× 133 0.8× 42 0.3× 20 1.4k
Bryan C. Mounce United States 20 488 0.5× 134 0.6× 420 1.9× 121 0.7× 232 1.5× 42 1.3k
Philippe Le Mercier Switzerland 19 601 0.6× 179 0.8× 612 2.8× 170 1.0× 206 1.3× 42 1.6k
Jinsong Sheng United States 18 426 0.4× 413 1.9× 293 1.4× 74 0.4× 204 1.3× 30 1.2k
Emily Locke United States 19 605 0.6× 143 0.7× 118 0.5× 57 0.3× 232 1.5× 42 1.3k
Christine Tait‐Burkard United Kingdom 13 480 0.5× 118 0.5× 584 2.7× 552 3.2× 141 0.9× 27 1.4k
Roberto Mateo United States 14 340 0.3× 74 0.3× 285 1.3× 106 0.6× 73 0.5× 24 920
Seesandra V. Rajagopala United States 13 511 0.5× 55 0.3× 92 0.4× 110 0.6× 81 0.5× 32 852

Countries citing papers authored by Matthew D. Dyer

Since Specialization
Citations

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

Fields of papers citing papers by Matthew D. Dyer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matthew D. Dyer

This figure shows the co-authorship network connecting the top 25 collaborators of Matthew D. Dyer. A scholar is included among the top collaborators of Matthew D. Dyer 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 D. Dyer. Matthew D. Dyer 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
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Sekhar, Hema, Matthew D. Dyer, Muhammad Imtiaz Khan, et al.. (2024). SF‐CORNER (splenic flexure colorectal cancer): an international survey of operative approaches and outcomes for cancers of the splenic flexure. Colorectal Disease. 26(4). 660–668.
3.
McGregor, Bradley A., S. Klijn, Flavia Ejzykowicz, et al.. (2023). Challenges in conducting fractional polynomial and standard parametric network meta-analyses of immune checkpoint inhibitors for first-line advanced renal cell carcinoma. Journal of Comparative Effectiveness Research. 12(8). e230004–e230004. 2 indexed citations
4.
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Gardner, Shea N., Kenneth G. Frey, C. L. Redden, et al.. (2015). Targeted amplification for enhanced detection of biothreat agents by next-generation sequencing. BMC Research Notes. 8(1). 682–682. 20 indexed citations
7.
Kim, Yon Hui, Han Liang, Xiuping Liu, et al.. (2012). AMPKα Modulation in Cancer Progression: Multilayer Integrative Analysis of the Whole Transcriptome in Asian Gastric Cancer. Cancer Research. 72(10). 2512–2521. 79 indexed citations
8.
Murali, T. M., et al.. (2012). Network-Based Prediction and Analysis of HIV Dependency Factors. Lecture notes in computer science. 7(9). 183–183. 37 indexed citations
9.
Dyer, Matthew D., T. M. Murali, & Bruno Sobral. (2011). Supervised learning and prediction of physical interactions between human and HIV proteins. Infection Genetics and Evolution. 11(5). 917–923. 71 indexed citations
10.
Murali, T. M., et al.. (2011). Network-Based Prediction and Analysis of HIV Dependency Factors. PLoS Computational Biology. 7(9). e1002164–e1002164. 38 indexed citations
11.
Dyer, Matthew D., Chris Neff, Max T. Dufford, et al.. (2010). The Human-Bacterial Pathogen Protein Interaction Networks of Bacillus anthracis, Francisella tularensis, and Yersinia pestis. PLoS ONE. 5(8). e12089–e12089. 108 indexed citations
12.
Rockx, Barry, Tracey Baas, Gregory A. Zornetzer, et al.. (2009). Early Upregulation of Acute Respiratory Distress Syndrome-Associated Cytokines Promotes Lethal Disease in an Aged-Mouse Model of Severe Acute Respiratory Syndrome Coronavirus Infection. Journal of Virology. 83(14). 7062–7074. 137 indexed citations
13.
Rockx, Barry, Tracey Baas, Gregory A. Zornetzer, et al.. (2009). Early Upregulation of Acute Respiratory Distress Syndrome-Associated Cytokines Promotes Lethal Disease in an Aged-Mouse Model of Severe Acute Respiratory Syndrome Coronavirus Infection. Journal of Virology. 83(17). 9022–9022. 4 indexed citations
14.
Goodman, Alan G., Jamie L. Fornek, Guruprasad R. Medigeshi, et al.. (2009). P58IPK: A Novel “CIHD” Member of the Host Innate Defense Response against Pathogenic Virus Infection. PLoS Pathogens. 5(5). e1000438–e1000438. 36 indexed citations
15.
Driscoll, Timothy, Matthew D. Dyer, T. M. Murali, & Bruno Sobral. (2008). PIG--the pathogen interaction gateway. Nucleic Acids Research. 37(Database). D647–D650. 45 indexed citations
16.
Dyer, Matthew D., T. M. Murali, & Bruno Sobral. (2008). The Landscape of Human Proteins Interacting with Viruses and Other Pathogens. PLoS Pathogens. 4(2). e32–e32. 256 indexed citations
17.
Dyer, Matthew D., T. M. Murali, & Bruno Sobral. (2007). Computational prediction of host-pathogen protein–protein interactions. Bioinformatics. 23(13). i159–i166. 137 indexed citations
18.
Sen, Shurjo K., Kyudong Han, Jianxin Wang, et al.. (2006). Human Genomic Deletions Mediated by Recombination between Alu Elements. The American Journal of Human Genetics. 79(1). 41–53. 237 indexed citations
19.
Zhou, Carol, et al.. (2006). MvirDB--a microbial database of protein toxins, virulence factors and antibiotic resistance genes for bio-defence applications. Nucleic Acids Research. 35(Database). D391–D394. 181 indexed citations
20.
Zhou, Carol, Jason R. Smith, Adam Zemła, et al.. (2006). MannDB – A microbial database of automated protein sequence analyses and evidence integration for protein characterization. BMC Bioinformatics. 7(1). 459–459. 2 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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