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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Short communication: Use of genomic and metabolic information as well as milk performance records for prediction of subclinical ketosis risk via artificial neural networks
Anita Ehret, David Hochstuhl, N. Krattenmacher, et al.
Journal of Dairy Science (2014) Vol. 98, Iss. 1, pp. 322-329
Open Access | Times Cited: 29

Showing 1-25 of 29 citing articles:

A review of deep learning applications for genomic selection
Osval A. Montesinos‐López, Abelardo Montesinos‐López, Paulino Pérez‐Rodríguez, et al.
BMC Genomics (2021) Vol. 22, Iss. 1
Open Access | Times Cited: 212
Reviewing methods of deep learning for intelligent healthcare systems in genomics and biomedicine
Imran Zafar, Shakila Anwar, Faheem kanwal, et al.
Biomedical Signal Processing and Control (2023) Vol. 86, pp. 105263-105263
Closed Access | Times Cited: 33
A review of machine learning models applied to genomic prediction in animal breeding
Narjice Chafai, Ichrak Hayah, Isidore Houaga, et al.
Frontiers in Genetics (2023) Vol. 14
Open Access | Times Cited: 24
Mining data from milk infrared spectroscopy to improve feed intake predictions in lactating dairy cows
João Ricardo Rebouças Dórea, Guilherme J. M. Rosa, K.A. Weld, et al.
Journal of Dairy Science (2018) Vol. 101, Iss. 7, pp. 5878-5889
Open Access | Times Cited: 70
Metabolomics and livestock genomics: Insights into a phenotyping frontier and its applications in animal breeding
Luca Fontanesi
Animal Frontiers (2016) Vol. 6, Iss. 1, pp. 73-79
Open Access | Times Cited: 67
A review of traditional and machine learning methods applied to animal breeding
Shadi Nayeri, Mehdi Sargolzaei, Dan Tulpan
Animal Health Research Reviews (2019) Vol. 20, Iss. 1, pp. 31-46
Closed Access | Times Cited: 65
A machine learning based decision aid for lameness in dairy herds using farm-based records
D. Warner, E. Vasseur, Daniel Lefebvre, et al.
Computers and Electronics in Agriculture (2020) Vol. 169, pp. 105193-105193
Closed Access | Times Cited: 55
Application of machine learning to improve dairy farm management: A systematic literature review
Naftali Slob, Cagatay Catal, Ayalew Kassahun
Preventive Veterinary Medicine (2020) Vol. 187, pp. 105237-105237
Open Access | Times Cited: 52
Applications of Omics Technology for Livestock Selection and Improvement
Dibyendu Chakraborty, Neelesh Sharma, Savleen Kour, et al.
Frontiers in Genetics (2022) Vol. 13
Open Access | Times Cited: 25
Candidate serum metabolite biomarkers of residual feed intake and carcass merit in sheep
Seyed Ali Goldansaz, Susan Markus, Mark Berjanskii, et al.
Journal of Animal Science (2020) Vol. 98, Iss. 10
Open Access | Times Cited: 32
In-Line Registered Milk Fat-to-Protein Ratio for the Assessment of Metabolic Status in Dairy Cows
Ramūnas Antanaitis, Karina Džermeikaitė, Vytautas Januškevičius, et al.
Animals (2023) Vol. 13, Iss. 20, pp. 3293-3293
Open Access | Times Cited: 10
Classification of healthy and mastitis Murrah buffaloes by application of neural network models using yield and milk quality parameters
Indu Panchal, I. K. Sawhney, A. K. Sharma, et al.
Computers and Electronics in Agriculture (2016) Vol. 127, pp. 242-248
Closed Access | Times Cited: 27
Symposium review: Dairy Brain—Informing decisions on dairy farms using data analytics
Michael C. Ferris, Adam Christensen, Steven R. Wangen
Journal of Dairy Science (2020) Vol. 103, Iss. 4, pp. 3874-3881
Open Access | Times Cited: 27
Polymorphisms within theAPOBRgene are highly associated with milk levels of prognostic ketosis biomarkers in dairy cows
Jens Tetens, Claas Heuer, Iris Heyer, et al.
Physiological Genomics (2015) Vol. 47, Iss. 4, pp. 129-137
Closed Access | Times Cited: 27
Genome-Enabled Prediction Methods Based on Machine Learning
Edgar L. Reinoso-Peláez, Daniel Gianola, Ó. González-Recio
Methods in molecular biology (2022), pp. 189-218
Open Access | Times Cited: 11
PreCowKetosis: A Shiny web application for predicting the risk of ketosis in dairy cows using prenatal indicators
Haoran Wang, Tingxian Guo, Zhenyu Wang, et al.
Computers and Electronics in Agriculture (2023) Vol. 206, pp. 107697-107697
Closed Access | Times Cited: 6
Integrating diverse data sources to predict disease risk in dairy cattle—a machine learning approach
Jana Lasser, Caspar Matzhold, C. Egger-Danner, et al.
Journal of Animal Science (2021) Vol. 99, Iss. 11
Open Access | Times Cited: 13
Dealing with complexity of new phenotypes in modern dairy cattle breeding
Anita Seidel, N. Krattenmacher, Georg Thaller
Animal Frontiers (2020) Vol. 10, Iss. 2, pp. 23-28
Open Access | Times Cited: 13
Screening for ketosis using multiple logistic regression based on milk yield and composition
Mitsunori Kayano, Tomoko Kataoka
Journal of Veterinary Medical Science (2015) Vol. 77, Iss. 11, pp. 1473-1478
Open Access | Times Cited: 10
Merging Metabolomics, Genetics, and Genomics in Livestock to Dissect Complex Production Traits
Luca Fontanesi
Springer eBooks (2016), pp. 43-62
Closed Access | Times Cited: 6
Short communication: Prediction of hyperketonemia in dairy cows in early lactation using on-farm cow data and net energy intake by partial least square discriminant analysis
Wei Xu, Edoardo Saccenti, Jacques Vervoort, et al.
Journal of Dairy Science (2020) Vol. 103, Iss. 7, pp. 6576-6582
Open Access | Times Cited: 5
Integrating diverse data sources to predict disease risk in dairy cattle
Jana Lasser, Caspar Matzhold, C. Egger-Danner, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2021)
Open Access | Times Cited: 5
Evaluation of Deep Learning for predicting rice traits using structural and single-nucleotide genomic variants
Ioanna‐Theoni Vourlaki, Sebastián E. Ramos‐Onsins, Miguel Pérez‐Enciso, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Prediction of Ketosis Using Radial Basis Function Neural Network in Dairy Cattle Farming
Edyta A. Bauer, W. Jagusiak
Preventive Veterinary Medicine (2024) Vol. 235, pp. 106410-106410
Open Access
Identifying healthy and mastitis Sahiwal cows using electro-chemical properties: A connectionist approach
Indu Panchal, I. K. Sawhney, A. K. Sharma
International Conference on Computing for Sustainable Global Development (2015), pp. 1185-1188
Closed Access | Times Cited: 2
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