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Evolution of Bayesian Methods in Animal Sciences with a Bibliometric Approach

Yıl 2025, Cilt: 8 Sayı: 6, 1759 - 1773, 15.11.2025
https://doi.org/10.34248/bsengineering.1743594

Öz

This study was conducted to examine the use of Bayesian statistical methods in published research in the field of animal sciences between 2000 and 2025 and to reveal thematic, structural and geographical trends in this field. A total of 1124 original research articles from the Web of Science database were analysed using the Bibliometrix R package and the Biblioshiny interface. Annual publication trends, most productive authors, countries and journals were examined, and structural and contextual patterns in literature were assessed through keyword co-occurrence network, thematic map and trend analysis. The prevalence of Bayesian methods in literature has increased significantly in the last 15 years. The USA, China, and Brazil have been identified as the most prolific publishing countries, while the Journal of Dairy Science and Genetics Selection Evolution has been identified as one of the most prolific journals. Thematic analysis revealed a concentration of methods on topics such as genetic value estimation, milk yield, genomic selection, diagnostic test analysis, and animal behavior. An analysis of the most frequently cited studies revealed the utilization of models such as BayesB, BayesRC, and BayesA. Bayesian methods are not only an alternative analysis approach but also an increasingly indispensable and powerful tool in animal sciences, thanks to their suitability for high-dimensional data, uncertain structures, and a priori knowledge. This bibliometric analysis reveals existing gaps in literature and the potential for improvement, providing strategic directions for future research in the field.

Etik Beyan

Ethics committee approval was not required for this study because there was no study on animals or humans.

Kaynakça

  • Andrés A. 2009. Measuring academic research: how to undertake a bibliometric study. Chandos Publishing, Oxford, UK, pp: 1-9.
  • Aria M, Cuccurullo C. 2017. Bibliometrix: An R-tool for comprehensive science mapping analysis. J Informetr, 11(4): 959-975.
  • Banchi P, Rota A, Bertero A, Domain G, Ali Hassan H, Lannoo J, Soom AV. 2022. Trends in small animal reproduction: a bibliometric analysis of the literature. Animals, 12(3): 336.
  • Basar EK. 2016. Analysis of agricultural experimental design by Bayesian methods. PhD Thesis, Akdeniz University, Institute of Science, Antalya, Türkiye, pp: 132.
  • Birkle C, Pendlebury D, Schnell J, Adams J. 2020. Web of science as a data source for research on scientific and scholarly activity. Quantit Sci Stud, 1(1): 363-376.
  • Branscum AJ, Gardner IA, Johnson WO. 2005. Estimation of diagnostic-test sensitivity and specificity through bayesian modelling. Prev Vet Med, 68(2–4): 145-63.
  • Çelik Ş. 2024. Bibliometric analysis of genomic selection in breeding of animal from 1993 to 2024: global trends and advancements. Front Genet, 24(15): 1402140.
  • Cui L, Tang W, Deng X, Jiang B. 2023. Farm animal welfare is a field of interest in china: a bibliometric analysis based on CiteSpace. Animals, 13(19): 3143.
  • Denwood MJ. 2016. runjags: An R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS. J Stat Softw, 71(9), 1–25.
  • Donthu H, Kumar S, Mukherjee D, Pandey N, Lim WM. 2021. How to conduct a bibliometric analysis: an overview and guidelines. J Bus Res, 133: 285-296.
  • Ekici O. 2009. İstatistikte bayesyen ve klasik yaklaşımın kavramsal farklılıkları. Balıkesir Üniv Sos Bil Enst Derg, 12(21): 89-101.
  • Gelman A, Carlin JB, Stern HS, Dunson DB, et al. 2013. Bayesian data analysis. CRC Press, Boca Raton, US, pp: 3-25.
  • Geman S, Geman D. 1984. Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell, 6(6): 721-741.
  • Gianola D, Fernando RL. 1986. Bayesian methods in animal breeding theory. J Anim Sci, 63: 217-244.
  • Gianola D. 2013. Priors in whole-genome regression: the bayesian alphabet returns. Genetics, 194(3): 573-596.
  • Habier D, Fernando RL, Kizilkaya K, Garrick DJ. 2011. Extension of the bayesian alphabet for genomic selection. BMC Bioinformatics, 12(186): 1-12.
  • Habier D, Tetens J, Seefried FR, Lichtner P, Thaller G. 2010. The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Genet Sel Evol, 42(5): 1-12.
  • Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME. 2009. Invited review: Genomic selection in dairy cattle: progress and challenges. J Dairy Sci, 92(2): 433-443.
  • Kruschke JK. 2021. Doing bayesian data analysis: a tutorial with R, JAGS, and Stan. Academic Press, New York, US, pp: 115-178.
  • Lee PM. 2011. Bayesian statistics: an introduction. Wiley, Hoboken, US, pp: 36-39.
  • MacLeod IM, Bowman PJ, Vander Jagt CJ, Haile Mariam M, Kemper KA, Chamberlain AJ, Schrooten C, Hayes BJ, Goddard ME. 2016. Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits. BMC Genomics, 17(144):1-21.
  • Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH. 1953. Equation of state calculations by fast computing machines. J Chem Phys, 21(6): 1087–-1092.
  • Meuwissen T, Goddard M. 2010. Accurate prediction of genetic values for complex traits by whole-genome resequencing. Genetics, 185(2): 623-631.
  • Meuwissen TH, Hayes BJ, Goddard ME. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157(4): 1819-1829.
  • Önder H, Tırınk C. 2022. Bibliometric analysis for genomic selection studies in animal science. J Inst Sci Technol, 12(3): 1849-1856.
  • Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D. 2021. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372(71): 1-9.
  • Sorensen D, Gianola D. 2002. Likelihood, bayesian, and mcmc methods in quantitative genetics. Springer Science and Business Media, New York, US, pp: 564-570.
  • Tohumcu V, Tulan Tohumcu D, Cengiz M. 2025. Subclinical endometritis in cows: a bibliometric analysis. Turk J Vet Anim Sci, 49(2): 59–69.
  • VanRaden PM. 2008. Efficient methods to compute genomic predictions. J Dairy Sci, 91(12): 4414-4422.
  • Yardibi F, Firat MZ, Teke EÇ. 2021. Trend topics in animal science: a bibliometric analysis using CiteSpace. Turk J Vet Anim Sci, 45(5): 833-840.

Evolution of Bayesian Methods in Animal Sciences with a Bibliometric Approach

Yıl 2025, Cilt: 8 Sayı: 6, 1759 - 1773, 15.11.2025
https://doi.org/10.34248/bsengineering.1743594

Öz

This study was conducted to examine the use of Bayesian statistical methods in published research in the field of animal sciences between 2000 and 2025 and to reveal thematic, structural and geographical trends in this field. A total of 1124 original research articles from the Web of Science database were analysed using the Bibliometrix R package and the Biblioshiny interface. Annual publication trends, most productive authors, countries and journals were examined, and structural and contextual patterns in literature were assessed through keyword co-occurrence network, thematic map and trend analysis. The prevalence of Bayesian methods in literature has increased significantly in the last 15 years. The USA, China, and Brazil have been identified as the most prolific publishing countries, while the Journal of Dairy Science and Genetics Selection Evolution has been identified as one of the most prolific journals. Thematic analysis revealed a concentration of methods on topics such as genetic value estimation, milk yield, genomic selection, diagnostic test analysis, and animal behavior. An analysis of the most frequently cited studies revealed the utilization of models such as BayesB, BayesRC, and BayesA. Bayesian methods are not only an alternative analysis approach but also an increasingly indispensable and powerful tool in animal sciences, thanks to their suitability for high-dimensional data, uncertain structures, and a priori knowledge. This bibliometric analysis reveals existing gaps in literature and the potential for improvement, providing strategic directions for future research in the field.

Etik Beyan

Ethics committee approval was not required for this study because there was no study on animals or humans.

Kaynakça

  • Andrés A. 2009. Measuring academic research: how to undertake a bibliometric study. Chandos Publishing, Oxford, UK, pp: 1-9.
  • Aria M, Cuccurullo C. 2017. Bibliometrix: An R-tool for comprehensive science mapping analysis. J Informetr, 11(4): 959-975.
  • Banchi P, Rota A, Bertero A, Domain G, Ali Hassan H, Lannoo J, Soom AV. 2022. Trends in small animal reproduction: a bibliometric analysis of the literature. Animals, 12(3): 336.
  • Basar EK. 2016. Analysis of agricultural experimental design by Bayesian methods. PhD Thesis, Akdeniz University, Institute of Science, Antalya, Türkiye, pp: 132.
  • Birkle C, Pendlebury D, Schnell J, Adams J. 2020. Web of science as a data source for research on scientific and scholarly activity. Quantit Sci Stud, 1(1): 363-376.
  • Branscum AJ, Gardner IA, Johnson WO. 2005. Estimation of diagnostic-test sensitivity and specificity through bayesian modelling. Prev Vet Med, 68(2–4): 145-63.
  • Çelik Ş. 2024. Bibliometric analysis of genomic selection in breeding of animal from 1993 to 2024: global trends and advancements. Front Genet, 24(15): 1402140.
  • Cui L, Tang W, Deng X, Jiang B. 2023. Farm animal welfare is a field of interest in china: a bibliometric analysis based on CiteSpace. Animals, 13(19): 3143.
  • Denwood MJ. 2016. runjags: An R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS. J Stat Softw, 71(9), 1–25.
  • Donthu H, Kumar S, Mukherjee D, Pandey N, Lim WM. 2021. How to conduct a bibliometric analysis: an overview and guidelines. J Bus Res, 133: 285-296.
  • Ekici O. 2009. İstatistikte bayesyen ve klasik yaklaşımın kavramsal farklılıkları. Balıkesir Üniv Sos Bil Enst Derg, 12(21): 89-101.
  • Gelman A, Carlin JB, Stern HS, Dunson DB, et al. 2013. Bayesian data analysis. CRC Press, Boca Raton, US, pp: 3-25.
  • Geman S, Geman D. 1984. Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell, 6(6): 721-741.
  • Gianola D, Fernando RL. 1986. Bayesian methods in animal breeding theory. J Anim Sci, 63: 217-244.
  • Gianola D. 2013. Priors in whole-genome regression: the bayesian alphabet returns. Genetics, 194(3): 573-596.
  • Habier D, Fernando RL, Kizilkaya K, Garrick DJ. 2011. Extension of the bayesian alphabet for genomic selection. BMC Bioinformatics, 12(186): 1-12.
  • Habier D, Tetens J, Seefried FR, Lichtner P, Thaller G. 2010. The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Genet Sel Evol, 42(5): 1-12.
  • Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME. 2009. Invited review: Genomic selection in dairy cattle: progress and challenges. J Dairy Sci, 92(2): 433-443.
  • Kruschke JK. 2021. Doing bayesian data analysis: a tutorial with R, JAGS, and Stan. Academic Press, New York, US, pp: 115-178.
  • Lee PM. 2011. Bayesian statistics: an introduction. Wiley, Hoboken, US, pp: 36-39.
  • MacLeod IM, Bowman PJ, Vander Jagt CJ, Haile Mariam M, Kemper KA, Chamberlain AJ, Schrooten C, Hayes BJ, Goddard ME. 2016. Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits. BMC Genomics, 17(144):1-21.
  • Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH. 1953. Equation of state calculations by fast computing machines. J Chem Phys, 21(6): 1087–-1092.
  • Meuwissen T, Goddard M. 2010. Accurate prediction of genetic values for complex traits by whole-genome resequencing. Genetics, 185(2): 623-631.
  • Meuwissen TH, Hayes BJ, Goddard ME. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157(4): 1819-1829.
  • Önder H, Tırınk C. 2022. Bibliometric analysis for genomic selection studies in animal science. J Inst Sci Technol, 12(3): 1849-1856.
  • Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D. 2021. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372(71): 1-9.
  • Sorensen D, Gianola D. 2002. Likelihood, bayesian, and mcmc methods in quantitative genetics. Springer Science and Business Media, New York, US, pp: 564-570.
  • Tohumcu V, Tulan Tohumcu D, Cengiz M. 2025. Subclinical endometritis in cows: a bibliometric analysis. Turk J Vet Anim Sci, 49(2): 59–69.
  • VanRaden PM. 2008. Efficient methods to compute genomic predictions. J Dairy Sci, 91(12): 4414-4422.
  • Yardibi F, Firat MZ, Teke EÇ. 2021. Trend topics in animal science: a bibliometric analysis using CiteSpace. Turk J Vet Anim Sci, 45(5): 833-840.
Toplam 30 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Biyoistatistik, İstatistiksel Analiz, Uygulamalı İstatistik, Ziraat Mühendisliği (Diğer)
Bölüm Research Articles
Yazarlar

Ebru Kaya Başar 0000-0001-6204-3143

Erken Görünüm Tarihi 12 Kasım 2025
Yayımlanma Tarihi 15 Kasım 2025
Gönderilme Tarihi 16 Temmuz 2025
Kabul Tarihi 19 Eylül 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 8 Sayı: 6

Kaynak Göster

APA Kaya Başar, E. (2025). Evolution of Bayesian Methods in Animal Sciences with a Bibliometric Approach. Black Sea Journal of Engineering and Science, 8(6), 1759-1773. https://doi.org/10.34248/bsengineering.1743594
AMA Kaya Başar E. Evolution of Bayesian Methods in Animal Sciences with a Bibliometric Approach. BSJ Eng. Sci. Kasım 2025;8(6):1759-1773. doi:10.34248/bsengineering.1743594
Chicago Kaya Başar, Ebru. “Evolution of Bayesian Methods in Animal Sciences with a Bibliometric Approach”. Black Sea Journal of Engineering and Science 8, sy. 6 (Kasım 2025): 1759-73. https://doi.org/10.34248/bsengineering.1743594.
EndNote Kaya Başar E (01 Kasım 2025) Evolution of Bayesian Methods in Animal Sciences with a Bibliometric Approach. Black Sea Journal of Engineering and Science 8 6 1759–1773.
IEEE E. Kaya Başar, “Evolution of Bayesian Methods in Animal Sciences with a Bibliometric Approach”, BSJ Eng. Sci., c. 8, sy. 6, ss. 1759–1773, 2025, doi: 10.34248/bsengineering.1743594.
ISNAD Kaya Başar, Ebru. “Evolution of Bayesian Methods in Animal Sciences with a Bibliometric Approach”. Black Sea Journal of Engineering and Science 8/6 (Kasım2025), 1759-1773. https://doi.org/10.34248/bsengineering.1743594.
JAMA Kaya Başar E. Evolution of Bayesian Methods in Animal Sciences with a Bibliometric Approach. BSJ Eng. Sci. 2025;8:1759–1773.
MLA Kaya Başar, Ebru. “Evolution of Bayesian Methods in Animal Sciences with a Bibliometric Approach”. Black Sea Journal of Engineering and Science, c. 8, sy. 6, 2025, ss. 1759-73, doi:10.34248/bsengineering.1743594.
Vancouver Kaya Başar E. Evolution of Bayesian Methods in Animal Sciences with a Bibliometric Approach. BSJ Eng. Sci. 2025;8(6):1759-73.

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