Emre Karaman

1.0k total citations
37 papers, 711 citations indexed

About

Emre Karaman is a scholar working on Genetics, Animal Science and Zoology and Plant Science. According to data from OpenAlex, Emre Karaman has authored 37 papers receiving a total of 711 indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Genetics, 17 papers in Animal Science and Zoology and 8 papers in Plant Science. Recurrent topics in Emre Karaman's work include Genetic and phenotypic traits in livestock (33 papers), Genetic Mapping and Diversity in Plants and Animals (17 papers) and Animal Nutrition and Physiology (11 papers). Emre Karaman is often cited by papers focused on Genetic and phenotypic traits in livestock (33 papers), Genetic Mapping and Diversity in Plants and Animals (17 papers) and Animal Nutrition and Physiology (11 papers). Emre Karaman collaborates with scholars based in Denmark, Türkiye and France. Emre Karaman's co-authors include Doğan Narinç, Tülin Aksoy, Mehmet Ziya Fırat, Guosheng Su, Mogens Sandø Lund, Ali Aygün, Hao Cheng, Dorian J. Garrick, Rohan L. Fernando and Şeymus Furat and has published in prestigious journals such as PLoS ONE, Scientific Reports and Journal of Dairy Science.

In The Last Decade

Emre Karaman

34 papers receiving 650 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Emre Karaman Denmark 17 462 373 203 67 49 37 711
Fábio Luiz Buranelo Toral Brazil 14 341 0.7× 202 0.5× 152 0.7× 157 2.3× 18 0.4× 71 527
Sandra Aidar de Queiroz Brazil 14 577 1.2× 226 0.6× 196 1.0× 191 2.9× 22 0.4× 70 682
Selma Forni Brazil 17 1.0k 2.3× 280 0.8× 475 2.3× 238 3.6× 45 0.9× 26 1.1k
Morteza Mokhtari Iran 15 726 1.6× 219 0.6× 131 0.6× 431 6.4× 18 0.4× 52 783
D. M. Ferguson Australia 11 379 0.8× 458 1.2× 49 0.2× 132 2.0× 15 0.3× 26 674
Gerardo Alves Fernandes Júnior Brazil 16 502 1.1× 186 0.5× 77 0.4× 147 2.2× 101 2.1× 34 586
Farhad Ghafouri‐Kesbi Iran 14 506 1.1× 160 0.4× 78 0.4× 251 3.7× 16 0.3× 48 547
L. A. F. Bezerra Brazil 13 373 0.8× 142 0.4× 89 0.4× 169 2.5× 15 0.3× 22 402
Akio Onogi Japan 15 530 1.1× 45 0.1× 562 2.8× 77 1.1× 22 0.4× 41 805
Obioha N Durunna Canada 10 333 0.7× 171 0.5× 67 0.3× 356 5.3× 15 0.3× 22 528

Countries citing papers authored by Emre Karaman

Since Specialization
Citations

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

Fields of papers citing papers by Emre Karaman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Emre Karaman

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

All Works

20 of 20 papers shown
1.
Clasen, Julie, W.F. Fikse, Guosheng Su, & Emre Karaman. (2023). Multibreed genomic prediction using summary statistics and a breed-origin-of-alleles approach. Heredity. 131(1). 33–42. 1 indexed citations
2.
Lund, Mogens Sandø, et al.. (2023). A breed-of-origin of alleles model that includes crossbred data improves predictive ability for crossbred animals in a multi-breed population. Genetics Selection Evolution. 55(1). 34–34. 3 indexed citations
3.
Garrick, Dorian J., et al.. (2022). XSim version 2: simulation of modern breeding programs. G3 Genes Genomes Genetics. 12(4). 9 indexed citations
4.
Su, Guosheng, et al.. (2022). Genomic prediction in Nordic Red dairy cattle considering breed origin of alleles. Journal of Dairy Science. 105(3). 2426–2438. 11 indexed citations
5.
Karaman, Emre, et al.. (2021). Genomic prediction using a reference population of multiple pure breeds and admixed individuals. Genetics Selection Evolution. 53(1). 46–46. 33 indexed citations
6.
Karaman, Emre, et al.. (2021). MeSCoT: the tool for quantitative trait simulation through the mechanistic modeling of genes’ regulatory interactions. G3 Genes Genomes Genetics. 11(7). 1 indexed citations
7.
Liu, Aoxing, Mogens Sandø Lund, Didier Boichard, et al.. (2020). Imputation for sequencing variants preselected to a customized low-density chip. Scientific Reports. 10(1). 9524–9524. 3 indexed citations
8.
Karaman, Emre, Mogens Sandø Lund, & Guosheng Su. (2019). Multi-trait single-step genomic prediction accounting for heterogeneous (co)variances over the genome. Heredity. 124(2). 274–287. 21 indexed citations
9.
Alexandre, Pâmela A., Laércio R. Porto-Neto, Emre Karaman, Sigrid A. Lehnert, & Antônio Reverter. (2019). Pooled genotyping strategies for the rapid construction of genomic reference populations1. Journal of Animal Science. 97(12). 4761–4769. 14 indexed citations
10.
Liu, Aoxing, Mogens Sandø Lund, Didier Boichard, et al.. (2019). Improvement of genomic prediction by integrating additional single nucleotide polymorphisms selected from imputed whole genome sequencing data. Heredity. 124(1). 37–49. 38 indexed citations
11.
Cemal, İ̇brahim, et al.. (2016). Bayesian inference of genetic parameters for ultrasound scanning traits of Kivircik lambs. animal. 11(3). 375–381. 8 indexed citations
12.
Karaman, Emre, et al.. (2015). Bayesian Hierarchical Modeling Applied to Test Day Milk Yield Data. 102–108. 1 indexed citations
13.
Narinç, Doğan, Ali Aygün, Emre Karaman, & Tülin Aksoy. (2015). Egg shell quality in Japanese quail: characteristics, heritabilities and genetic and phenotypic relationships. animal. 9(7). 1091–1096. 17 indexed citations
14.
Narinç, Doğan, Emre Karaman, & Tülin Aksoy. (2014). Effects of slaughter age and mass selection on slaughter and carcass characteristics in 2 lines of Japanese quail. Poultry Science. 93(3). 762–769. 20 indexed citations
15.
Karaman, Emre & Mehmet Ziya Fırat. (2013). Modeling the test day milk yields via time series method.. Kafkas Universitesi Veteriner Fakultesi Dergisi. 19(4). 659–664. 1 indexed citations
16.
Narinç, Doğan, Tülin Aksoy, Emre Karaman, et al.. (2013). Japanese quail meat quality: Characteristics, heritabilities, and genetic correlations with some slaughter traits. Poultry Science. 92(7). 1735–1744. 57 indexed citations
17.
Narinç, Doğan & Emre Karaman. (2011). Kanatlı Hayvan Islahında Akrabalık ve SAS Programı ile Akrabalık Matrislerinin Oluşturulması. DergiPark (Istanbul University). 1 indexed citations
18.
Narinç, Doğan, et al.. (2010). Analysis of Fitting Growth Models in Medium Growing Chicken Raised Indoor System. Animal and Veterinary Sciences. 1(1). 12–18. 29 indexed citations
19.
Yol, Engin, Emre Karaman, Şeymus Furat, & Bülent Uzun. (2010). Assessment of Selection Criteria in Sesame by Using Correlation Coefficients, Path and Factor Analyses. Australian Journal of Crop Science. 4(8). 598–602. 44 indexed citations
20.
Narinç, Doğan, Tülin Aksoy, Emre Karaman, & Kemal Karabağ. (2009). Japon Bıldırcınlarında Yüksek Canlı Ağırlık Yönünde Uygulanan Seleksiyonun Büyüme Parametreleri Üzerine Etkisi. Mediterranean Agricultural Sciences. 22(2). 149–156.

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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