Emily A. King

954 total citations · 1 hit paper
6 papers, 417 citations indexed

About

Emily A. King is a scholar working on Molecular Biology, Genetics and Statistics and Probability. According to data from OpenAlex, Emily A. King has authored 6 papers receiving a total of 417 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Molecular Biology, 4 papers in Genetics and 2 papers in Statistics and Probability. Recurrent topics in Emily A. King's work include Genetic Associations and Epidemiology (4 papers), Statistical Methods and Bayesian Inference (1 paper) and Advanced Biosensing Techniques and Applications (1 paper). Emily A. King is often cited by papers focused on Genetic Associations and Epidemiology (4 papers), Statistical Methods and Bayesian Inference (1 paper) and Advanced Biosensing Techniques and Applications (1 paper). Emily A. King collaborates with scholars based in United States, Germany and China. Emily A. King's co-authors include J. Wade Davis, Jacob F. Degner, Yue Yu, Karla V. Ballman, Zhi Wei, Diane E. Grill, Jun Chen, Rebecca A. Deek, Sujana Ghosh and Ammar J. Alsheikh and has published in prestigious journals such as Bioinformatics, PLoS Genetics and BMC Bioinformatics.

In The Last Decade

Emily A. King

5 papers receiving 412 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval

2019 309 citations Emily A. King, J. Wade Davis et al. PLoS Genetics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Emily A. King United States	4	241	171	34	33	31	6	417
Nadia M. Penrod United States	9	323 1.3×	203 1.2×	32 0.9×	27 0.8×	65 2.1×	15	597
Alfredo Miranda United Kingdom	2	302 1.3×	184 1.1×	45 1.3×	62 1.9×	73 2.4×	2	507
Luca Fumis United Kingdom	2	302 1.3×	184 1.1×	45 1.3×	62 1.9×	73 2.4×	4	507
Miguel Carmona United Kingdom	4	308 1.3×	186 1.1×	46 1.4×	62 1.9×	80 2.6×	7	539
Nate Barney United States	4	341 1.4×	289 1.7×	35 1.0×	7 0.2×	37 1.2×	5	594
L. J. Sheffield Australia	11	267 1.1×	193 1.1×	27 0.8×	15 0.5×	29 0.9×	16	490
Joseph V. Haas United States	10	164 0.7×	39 0.2×	25 0.7×	22 0.7×	53 1.7×	20	513
Snehal Patil United States	10	310 1.3×	104 0.6×	14 0.4×	11 0.3×	19 0.6×	23	518
Christopher Hardt Germany	7	239 1.0×	44 0.3×	40 1.2×	29 0.9×	33 1.1×	10	374
Mercè Garcia-Barceló Hong Kong	17	284 1.2×	165 1.0×	37 1.1×	7 0.2×	32 1.0×	26	881

Countries citing papers authored by Emily A. King

Since Specialization

Citations

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

Fields of papers citing papers by Emily A. King

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Emily A. King

This figure shows the co-authorship network connecting the top 25 collaborators of Emily A. King. A scholar is included among the top collaborators of Emily A. 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 Emily A. King. Emily A. 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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6 of 6 papers shown

1.

Riley‐Gillis, Bridget, Shirng‐Wern Tsaih, Emily A. King, et al.. (2023). Machine learning reveals genetic modifiers of the immune microenvironment of cancer. iScience. 26(9). 107576–107576.

2.

Alsheikh, Ammar J., Emily A. King, Jonas Reeb, et al.. (2022). The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases. BMC Medical Genomics. 15(1). 74–74. 39 indexed citations

3.

King, Emily A., et al.. (2021). Estimating colocalization probability from limited summary statistics. BMC Bioinformatics. 22(1). 254–254. 6 indexed citations

4.

King, Emily A., J. Wade Davis, & Jacob F. Degner. (2019). Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval. PLoS Genetics. 15(12). e1008489–e1008489. 309 indexed citations breakdown →

5.

Chen, Jun, Emily A. King, Rebecca A. Deek, et al.. (2017). An omnibus test for differential distribution analysis of microbiome sequencing data. Bioinformatics. 34(4). 643–651. 62 indexed citations

6.

Kräemer, Stephan, Glenn M. Sanders, Evaldas Katilius, et al.. (2014). SOMAscanTM v3.0: A Sensitive and Reproducible, Multiplex Proteomic Platform that Measures over 1100 Analytes in Complex Matrices. Journal of Biomolecular Techniques JBT. 25. 1 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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