Phil Bowman

2.6k total citations
10 papers, 832 citations indexed

About

Phil Bowman is a scholar working on Genetics, Plant Science and Animal Science and Zoology. According to data from OpenAlex, Phil Bowman has authored 10 papers receiving a total of 832 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Genetics, 4 papers in Plant Science and 3 papers in Animal Science and Zoology. Recurrent topics in Phil Bowman's work include Genetic and phenotypic traits in livestock (10 papers), Genetic Mapping and Diversity in Plants and Animals (7 papers) and Reproductive Physiology in Livestock (3 papers). Phil Bowman is often cited by papers focused on Genetic and phenotypic traits in livestock (10 papers), Genetic Mapping and Diversity in Plants and Animals (7 papers) and Reproductive Physiology in Livestock (3 papers). Phil Bowman collaborates with scholars based in Australia, Germany and United States. Phil Bowman's co-authors include Ben J. Hayes, J.E. Pryce, Michael E. Goddard, Amanda J. Chamberlain, M. Haile‐Mariam, Thuy T. T. Nguyen, Iona M. MacLeod, Keith W. Savin, Tad S. Sonstegard and Curtis P. Van Tassell and has published in prestigious journals such as PLoS ONE, Journal of Dairy Science and PLoS Genetics.

In The Last Decade

Phil Bowman

10 papers receiving 810 citations

Peers

Phil Bowman

GENET

ASZ

ACS

J.B.C.H.M. van Kaam Italy

Sarah Laguna Conceição Meirelles Brazil

Eui-Soo Kim United States

B. E. Harrison Australia

Annaíza Braga Bignardi Brazil

Raffaella Finocchiaro Italy

C. de U. Magnabosco Brazil

Ana Fabrícia Braga Magalhães Brazil

Kaixing Qu China

Gregório Miguel Ferreira de Camargo Brazil

J.B.C.H.M. van Kaam Italy

Phil Bowman

762 ×1.2

GENET

274 ×0.9

ASZ

363 ×2.0

151 ×0.9

ACS

46 ×0.5

Citations per year, relative to Phil Bowman Phil Bowman (= 1×) peers J.B.C.H.M. van Kaam

Countries citing papers authored by Phil Bowman

Since Specialization

Citations

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

Fields of papers citing papers by Phil Bowman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Phil Bowman

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

All Works

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10 of 10 papers shown

Xiang, Ruidong, Ben J. Hayes, Christy J. Vander Jagt, et al.. (2018). Genome variants associated with RNA splicing variations in bovine are extensively shared between tissues. BMC Genomics. 19(1). 521–521. 29 indexed citations

Nguyen, Thuy T. T., Phil Bowman, M. Haile‐Mariam, et al.. (2017). Short communication: Implementation of a breeding value for heat tolerance in Australian dairy cattle. Journal of Dairy Science. 100(9). 7362–7367. 53 indexed citations

Pausch, Hubert, Iona M. MacLeod, Ruedi Fries, et al.. (2017). Evaluation of the accuracy of imputed sequence variant genotypes and their utility for causal variant detection in cattle. Genetics Selection Evolution. 49(1). 24–24. 76 indexed citations

Berg, Irene van den, Phil Bowman, Iona M. MacLeod, et al.. (2017). Multi-breed genomic prediction using Bayes R with sequence data and dropping variants with a small effect. Genetics Selection Evolution. 49(1). 70–70. 31 indexed citations

Nguyen, Thuy T. T., Phil Bowman, M. Haile‐Mariam, J.E. Pryce, & Ben J. Hayes. (2016). Genomic selection for tolerance to heat stress in Australian dairy cattle. Journal of Dairy Science. 99(4). 2849–2862. 167 indexed citations

Chen, Yi‐Ping Phoebe, et al.. (2016). A hybrid expectation maximisation and MCMC sampling algorithm to implement Bayesian mixture model based genomic prediction and QTL mapping. BMC Genomics. 17(1). 744–744. 17 indexed citations

González-Recio, Óscar, Hans D. Daetwyler, Iona M. MacLeod, et al.. (2015). Rare Variants in Transcript and Potential Regulatory Regions Explain a Small Percentage of the Missing Heritability of Complex Traits in Cattle. PLoS ONE. 10(12). e0143945–e0143945. 16 indexed citations

Hayes, Ben J., J.E. Pryce, Amanda J. Chamberlain, Phil Bowman, & Michael E. Goddard. (2010). Genetic Architecture of Complex Traits and Accuracy of Genomic Prediction: Coat Colour, Milk-Fat Percentage, and Type in Holstein Cattle as Contrasting Model Traits. PLoS Genetics. 6(9). e1001139–e1001139. 315 indexed citations

Hayes, Ben J., Phil Bowman, Amanda J. Chamberlain, et al.. (2009). A Validated Genome Wide Association Study to Breed Cattle Adapted to an Environment Altered by Climate Change. PLoS ONE. 4(8). e6676–e6676. 115 indexed citations

10.

Haile‐Mariam, M., Phil Bowman, & Michael E. Goddard. (2007). A practical approach for minimising inbreeding and maximising genetic gain in dairy cattle. Genetics Selection Evolution. 39(4). 369–389. 13 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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