Michael R. Brent

36.6k total citations
95 papers, 3.8k citations indexed

About

Michael R. Brent is a scholar working on Molecular Biology, Artificial Intelligence and Plant Science. According to data from OpenAlex, Michael R. Brent has authored 95 papers receiving a total of 3.8k indexed citations (citations by other indexed papers that have themselves been cited), including 48 papers in Molecular Biology, 21 papers in Artificial Intelligence and 15 papers in Plant Science. Recurrent topics in Michael R. Brent's work include Genomics and Phylogenetic Studies (27 papers), RNA and protein synthesis mechanisms (22 papers) and Genomics and Chromatin Dynamics (16 papers). Michael R. Brent is often cited by papers focused on Genomics and Phylogenetic Studies (27 papers), RNA and protein synthesis mechanisms (22 papers) and Genomics and Chromatin Dynamics (16 papers). Michael R. Brent collaborates with scholars based in United States, United Kingdom and Denmark. Michael R. Brent's co-authors include Timothy A. Cartwright, Jeffrey Mark Siskind, Paul Flicek, Ian Korf, Jennifer Ganger, Samuel S. Gross, Delphine Dahan, Roderic Guigó, Brian C. Haynes and Matthew Snover and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Journal of Biological Chemistry.

In The Last Decade

Michael R. Brent

90 papers receiving 3.5k citations

Peers

Michael R. Brent

DEP

ECP

John S. Logan United States

Andrew Wedel United States

Sabine Stoll Germany

Nick Campbell Ireland

W. Einar Gall United States

Christian Hacker United Kingdom

Mark Schroeder United States

J. J. McDowell United States

Chris Miles United States

James L. Morgan United States

John S. Logan United States

Michael R. Brent

1.8k ×1.1

558 ×0.6

612 ×0.6

DEP

1.4k ×3.0

ECP

41 ×0.1

Citations per year, relative to Michael R. Brent Michael R. Brent (= 1×) peers John S. Logan

Countries citing papers authored by Michael R. Brent

Since Specialization

Citations

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

Fields of papers citing papers by Michael R. Brent

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael R. Brent

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

All Works

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20 of 20 papers shown

Feitosa, Mary F., Mary K. Wojczynski, Shiow Jiuan Lin, et al.. (2024). A methodology for gene level omics-WAS integration identifies genes influencing traits associated with cardiovascular risks: the Long Life Family Study. Human Genetics. 143(9-10). 1241–1252.

Brent, Michael R., et al.. (2023). NetProphet 3: a machine learning framework for transcription factor network mapping and multi-omics integration. Bioinformatics. 39(2). 2 indexed citations

Hayashi, Tomohiro, Kory J. Lavine, Michael R. Brent, et al.. (2022). The Programmed Death-1 Signaling Axis Modulates Inflammation and LV Structure/Function in a Stress-Induced Cardiomyopathy Model. JACC Basic to Translational Science. 7(11). 1120–1139. 21 indexed citations

Chang, Andrew L., et al.. (2021). The Transcription Factor Pdr802 Regulates Titan Cell Formation and Pathogenicity of Cryptococcus neoformans. mBio. 12(2). 18 indexed citations

Brent, Michael R., et al.. (2020). Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data. Bioinformatics. 37(9). 1234–1245. 19 indexed citations

Kang, Yiming, Nikhil Patel, Xuhua Chen, et al.. (2020). Dual threshold optimization and network inference reveal convergent evidence from TF binding locations and TF perturbation responses. Genome Research. 30(3). 459–471. 17 indexed citations

Maier, Ezekiel J., Brian C. Haynes, Felipe H. Santiago‐Tirado, et al.. (2016). Computational Analysis Reveals a Key Regulator of Cryptococcal Virulence and Determinant of Host Response. mBio. 7(2). e00313–16. 32 indexed citations

Maier, Ezekiel J., Brian C. Haynes, Zhuo A. Wang, et al.. (2015). Model-driven mapping of transcriptional networks reveals the circuitry and dynamics of virulence regulation. Genome Research. 25(5). 690–700. 37 indexed citations

Haynes, Brian C., et al.. (2013). Mapping functional transcription factor networks from gene expression data. Genome Research. 23(8). 1319–1328. 33 indexed citations

10.

Chiappinelli, Katherine B., Brian C. Haynes, Michael R. Brent, & Paul J. Goodfellow. (2012). Reduced DICER1 Elicits an Interferon Response in Endometrial Cancer Cells. Molecular Cancer Research. 10(3). 316–325. 15 indexed citations

11.

Chen, Minmin, Yixin Chen, & Michael R. Brent. (2008). CRF-OPT: an efficient high-quality conditional random field solver. National Conference on Artificial Intelligence. 1018–1023. 3 indexed citations

12.

Brent, Michael R.. (2007). How does eukaryotic gene prediction work?. Nature Biotechnology. 25(8). 883–885. 22 indexed citations

13.

Tenney, Aaron, et al.. (2007). A tale of two templates: Automatically resolving double traces has many applications, including efficient PCR-based elucidation of alternative splices. Genome Research. 17(2). 212–218. 12 indexed citations

14.

Baren, Marijke J. van & Michael R. Brent. (2006). Iterative gene prediction and pseudogene removal improves genome annotation. Genome Research. 16(5). 678–685. 47 indexed citations

15.

Wei, Chaochun, Philippe Lamesch, Manimozhiyan Arumugam, et al.. (2005). Closing in on the C. elegans ORFeome by cloning TWINSCAN predictions. Genome Research. 15(4). 577–582. 36 indexed citations

16.

Wu, Jia Qian, David Shteynberg, Manimozhiyan Arumugam, Richard A. Gibbs, & Michael R. Brent. (2004). Identification of Rat Genes by TWINSCAN Gene Prediction, RT–PCR, and Direct Sequencing. Genome Research. 14(4). 665–671. 25 indexed citations

17.

Flicek, Paul, et al.. (2003). Leveraging the Mouse Genome for Gene Prediction in Human: From Whole-Genome Shotgun Reads to a Global Synteny Map. Genome Research. 13(1). 46–54. 76 indexed citations

18.

Snover, Matthew & Michael R. Brent. (2002). A Probabilistic Model for Learning Concatenative Morphology. Neural Information Processing Systems. 15. 1537–1544. 14 indexed citations

19.

Brent, Michael R. & Jeffrey Mark Siskind. (2001). The role of exposure to isolated words in early vocabulary development. Cognition. 81(2). B33–B44. 324 indexed citations

20.

Brent, Michael R., Timothy A. Cartwright, & Adamantios I. Gafos. (1996). Distributional regularity and phonotactics are useful for early lexical acquisition. 61(Pt 12). 1993–2000. 4 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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