Brian R. King

415 total citations
18 papers, 293 citations indexed

About

Brian R. King is a scholar working on Molecular Biology, Artificial Intelligence and Aging. According to data from OpenAlex, Brian R. King has authored 18 papers receiving a total of 293 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 3 papers in Artificial Intelligence and 3 papers in Aging. Recurrent topics in Brian R. King's work include Machine Learning in Bioinformatics (7 papers), Genomics and Phylogenetic Studies (6 papers) and RNA and protein synthesis mechanisms (4 papers). Brian R. King is often cited by papers focused on Machine Learning in Bioinformatics (7 papers), Genomics and Phylogenetic Studies (6 papers) and RNA and protein synthesis mechanisms (4 papers). Brian R. King collaborates with scholars based in United States. Brian R. King's co-authors include Chittibabu Guda, Tae-Hwan Kim, Sanjit Pandey, Greg J. Hermann, Erin Currie, Lena K. Schroeder, Satish Srinivasan, Aaron M. Kershner, Lipika R. Pal and Maurice F. Aburdene and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Genetics.

In The Last Decade

Brian R. King

16 papers receiving 286 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Brian R. King United States	8	148	61	35	34	28	18	293
Kerui Huang China	10	123 0.8×	49 0.8×	52 1.5×	21 0.6×	36 1.3×	34	363
Noah M. Daniels United States	12	240 1.6×	74 1.2×	8 0.2×	39 1.1×	3 0.1×	27	383
Yonghong Yan China	11	252 1.7×	28 0.5×	65 1.9×	5 0.1×	52 1.9×	25	400
Zeina R. Al Sayed France	4	295 2.0×	47 0.8×	7 0.2×	11 0.3×	5 0.2×	6	412
Dirk Tomandl United States	6	113 0.8×	27 0.4×	8 0.2×	18 0.5×	2 0.1×	7	284
Hyeonjin Kim South Korea	10	139 0.9×	35 0.6×	49 1.4×	29 0.9×	6 0.2×	20	300
C. Michael Lewis United States	7	199 1.3×	39 0.6×	50 1.4×	2 0.1×	46 1.6×	19	350
Advait Balaji United States	8	188 1.3×	40 0.7×	33 0.9×	6 0.2×	3 0.1×	14	307

Countries citing papers authored by Brian R. King

Since Specialization

Citations

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

Fields of papers citing papers by Brian R. King

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Brian R. King

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

All Works

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18 of 18 papers shown

King, Brian R., et al.. (2023). Warped Dynamic Linear Models for Time Series of Counts. Bayesian Analysis. 20(1). 2 indexed citations

Kim, Tae-Hwan & Brian R. King. (2020). Time series prediction using deep echo state networks. Neural Computing and Applications. 32(23). 17769–17787. 73 indexed citations

King, Brian R., et al.. (2020). Teaching Data Mining in the Era of Big Data.

Myers, Scott M., Aaron D. Mitchel, Brian R. King, et al.. (2019). Within-task variability on standardized language tests predicts autism spectrum disorder: a pilot study of the Response Dispersion Index. Journal of Neurodevelopmental Disorders. 11(1). 21–21. 3 indexed citations

King, Brian R., et al.. (2019). An ABCG Transporter Functions in Rab Localization and Lysosome-Related Organelle Biogenesis inCaenorhabditis elegans. Genetics. 214(2). 419–445. 5 indexed citations

King, Brian R.. (2016). Transnational Actors in World Politics. 159–184. 1 indexed citations

King, Brian R., et al.. (2014). Application of discrete Fourier inter-coefficient difference for assessing genetic sequence similarity. PubMed. 2014(1). 8–8. 11 indexed citations

King, Brian R., et al.. (2014). International Arbitrators as Lawmakers. 2 indexed citations

Srinivasan, Satish, et al.. (2013). Mining for class-specific motifs in protein sequence classification. BMC Bioinformatics. 14(1). 96–96. 15 indexed citations

10.

King, Brian R., et al.. (2012). ngLOC: software and web server for predicting protein subcellular localization in prokaryotes and eukaryotes. BMC Research Notes. 5(1). 351–351. 40 indexed citations

11.

King, Brian R., et al.. (2012). Diagnostic Accuracy Comparing White Light to Narrow Band Imaging in Identification of Non-Adenomatous and Adenomatous Polyps in a Community-Based Setting. The American Journal of Gastroenterology. 107. S794–S794. 1 indexed citations

12.

Levitte, Steven, et al.. (2010). ACaenorhabditis elegansmodel of orotic aciduria reveals enlarged lysosome‐related organelles in embryos lackingumps‐1function. FEBS Journal. 277(6). 1420–1439. 15 indexed citations

13.

Guda, Chittibabu, et al.. (2009). A Top-Down Approach to Infer and Compare Domain-Domain Interactions across Eight Model Organisms. PLoS ONE. 4(3). e5096–e5096. 12 indexed citations

14.

King, Brian R., Lance Latham, & Chittibabu Guda. (2009). Estimation of Subcellular Proteomes in Bacterial Species. 3(1). 1–11. 3 indexed citations

15.

King, Brian R. & Chittibabu Guda. (2008). Semi-Supervised Learning for Classification of Protein Sequence Data. SHILAP Revista de lepidopterología. 1 indexed citations

16.

King, Brian R. & Chittibabu Guda. (2008). Semi-Supervised Learning for Classification of Protein Sequence Data. Scientific Programming. 16(1). 5–29. 6 indexed citations

17.

King, Brian R. & Chittibabu Guda. (2007). ngLOC: an n-gram-based Bayesian method for estimating the subcellular proteomes of eukaryotes. Genome biology. 8(5). R68–R68. 79 indexed citations

18.

Currie, Erin, et al.. (2007). Role of the Caenorhabditis elegans Multidrug Resistance Gene, mrp-4, in Gut Granule Differentiation. Genetics. 177(3). 1569–1582. 24 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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