Jack Kamm

4.6k total citations · 1 hit paper
15 papers, 951 citations indexed

About

Jack Kamm is a scholar working on Genetics, Molecular Biology and Infectious Diseases. According to data from OpenAlex, Jack Kamm has authored 15 papers receiving a total of 951 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Genetics, 7 papers in Molecular Biology and 5 papers in Infectious Diseases. Recurrent topics in Jack Kamm's work include Genetic diversity and population structure (5 papers), Forensic and Genetic Research (4 papers) and SARS-CoV-2 and COVID-19 Research (3 papers). Jack Kamm is often cited by papers focused on Genetic diversity and population structure (5 papers), Forensic and Genetic Research (4 papers) and SARS-CoV-2 and COVID-19 Research (3 papers). Jack Kamm collaborates with scholars based in United States, United Kingdom and Switzerland. Jack Kamm's co-authors include Yun S. Song, Jonathan Terhorst, Jeffrey P. Spence, Richard Durbin, Matthias Steinrücken, Joshua D. Reuther, Martin Sikora, Anna‐Sapfo Malaspinas, Ripan S. Malhi and Joel D. Irish and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nature Genetics.

In The Last Decade

Jack Kamm

15 papers receiving 940 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.

Robust and scalable inference of population history from hundreds of unphased whole genomes

2016 489 citations Jonathan Terhorst, Jack Kamm et al. Nature Genetics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jack Kamm United States	10	630	269	116	113	92	15	951
Flora Jay France	13	730 1.2×	223 0.8×	166 1.4×	70 0.6×	99 1.1×	23	1.1k
Michael DeGiorgio United States	20	865 1.4×	470 1.7×	141 1.2×	124 1.1×	110 1.2×	57	1.2k
Jonathan Terhorst United States	11	749 1.2×	325 1.2×	125 1.1×	117 1.0×	127 1.4×	26	1.1k
Alison Devault United States	11	372 0.6×	502 1.9×	174 1.5×	176 1.6×	105 1.1×	20	987
Yun-Xin Fu United States	17	582 0.9×	385 1.4×	146 1.3×	127 1.1×	35 0.4×	35	1.0k
José Alfredo Samaniego Castruita Denmark	18	623 1.0×	527 2.0×	429 3.7×	237 2.1×	202 2.2×	33	1.4k
Stefano Mona France	22	888 1.4×	298 1.1×	335 2.9×	88 0.8×	90 1.0×	43	1.3k
Andrea Benazzo Italy	19	562 0.9×	227 0.8×	205 1.8×	137 1.2×	57 0.6×	35	965
Christian Carøe Denmark	12	362 0.6×	620 2.3×	532 4.6×	131 1.2×	93 1.0×	26	1.3k
Jerome Kelleher United Kingdom	16	947 1.5×	386 1.4×	89 0.8×	85 0.8×	52 0.6×	31	1.2k

Countries citing papers authored by Jack Kamm

Since Specialization

Citations

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

Fields of papers citing papers by Jack Kamm

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jack Kamm. 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 Jack Kamm. The network helps show where Jack Kamm may publish in the future.

Co-authorship network of co-authors of Jack Kamm

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

All Works

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15 of 15 papers shown

Madera, Sharline, Paula Hayakawa Serpa, Jack Kamm, et al.. (2022). Prolonged silent carriage, genomic virulence potential and transmission between staff and patients characterize a neonatal intensive care unit (NICU) outbreak of methicillin-resistant Staphylococcus aureus (MRSA). Infection Control and Hospital Epidemiology. 44(1). 40–46. 10 indexed citations

Black, Allison, Patrick Ayscue, Jack Kamm, et al.. (2022). Using genomic epidemiology of SARS-CoV-2 to support contact tracing and public health surveillance in rural Humboldt County, California. BMC Public Health. 22(1). 456–456. 9 indexed citations

Mick, Eran, Jack Kamm, Anthea Mitchell, et al.. (2022). A 2-Gene Host Signature for Improved Accuracy of COVID-19 Diagnosis Agnostic to Viral Variants. mSystems. 8(1). e0067122–e0067122. 6 indexed citations

Fridey, Joy, Susan L. Stramer, Ashok Nambiar, et al.. (2020). Sepsis from an apheresis platelet contaminated with Acinetobacter calcoaceticus/baumannii complex bacteria and Staphylococcus saprophyticus after pathogen reduction. Transfusion. 60(9). 1960–1969. 18 indexed citations

Leligdowicz, Aleksandra, Jack Kamm, Alejandra Jáuregui, et al.. (2020). Physiologically Derived Transcriptional Signature of Endotoxin Tolerance Identifies Immune Suppression in Early Sepsis. A2586–A2586. 1 indexed citations

Saha, Senjuti, Roly Malaker, Mohammad Saiful Islam Sajib, et al.. (2020). Complete Genome Sequence of a Novel Coronavirus (SARS-CoV-2) Isolate from Bangladesh. Microbiology Resource Announcements. 9(24). 26 indexed citations

Steinrücken, Matthias, Jack Kamm, Jeffrey P. Spence, & Yun S. Song. (2019). Inference of complex population histories using whole-genome sequences from multiple populations. Proceedings of the National Academy of Sciences. 116(34). 17115–17120. 38 indexed citations

Kamm, Jack, Jonathan Terhorst, Richard Durbin, & Yun S. Song. (2019). Efficiently Inferring the Demographic History of Many Populations With Allele Count Data. Journal of the American Statistical Association. 115(531). 1472–1487. 89 indexed citations

Steinrücken, Matthias, et al.. (2018). Model‐based detection and analysis of introgressed Neanderthal ancestry in modern humans. Molecular Ecology. 27(19). 3873–3888. 43 indexed citations

10.

Moreno-Mayar, J. Víctor, Ben A. Potter, Lasse Vinner, et al.. (2018). Terminal Pleistocene Alaskan genome reveals first founding population of Native Americans. Nature. 553(7687). 203–207. 176 indexed citations

11.

Spence, Jeffrey P., Jack Kamm, & Yun S. Song. (2016). The Site Frequency Spectrum for General Coalescents. Genetics. 202(4). 1549–1561. 24 indexed citations

12.

Terhorst, Jonathan, Jack Kamm, & Yun S. Song. (2016). Robust and scalable inference of population history from hundreds of unphased whole genomes. Nature Genetics. 49(2). 303–309. 489 indexed citations breakdown →

13.

Harris, Kelley, Sara Sheehan, Jack Kamm, & Yun S. Song. (2014). Decoding Coalescent Hidden Markov Models in Linear Time. Lecture notes in computer science. 8394. 100–114. 6 indexed citations

14.

Bhaskar, Anand, Jack Kamm, & Yun S. Song. (2012). Approximate Sampling Formulae for General Finite-Alleles Models of Mutation. Advances in Applied Probability. 44(2). 408–428. 9 indexed citations

15.

Bhaskar, Anand, Jack Kamm, & Yun S. Song. (2012). Approximate Sampling Formulae for General Finite-Alleles Models of Mutation. Advances in Applied Probability. 44(2). 408–428. 7 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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