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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:

Predicting phenotypes from genetic, environment, management, and historical data using CNNs
Jacob D. Washburn, Emre Çimen, Guillaume P. Ramstein, et al.
Theoretical and Applied Genetics (2021) Vol. 134, Iss. 12, pp. 3997-4011
Closed Access | Times Cited: 31

Showing 1-25 of 31 citing articles:

Breeding crops for drought-affected environments and improved climate resilience
Mark Cooper, Carlos D. Messina
The Plant Cell (2022) Vol. 35, Iss. 1, pp. 162-186
Open Access | Times Cited: 78
Yield prediction through integration of genetic, environment, and management data through deep learning
Daniel R Kick, Jason G. Wallace, James C. Schnable, et al.
G3 Genes Genomes Genetics (2023) Vol. 13, Iss. 4
Open Access | Times Cited: 30
Machine learning for predicting phenotype from genotype and environment
Tingting Guo, Xianran Li
Current Opinion in Biotechnology (2022) Vol. 79, pp. 102853-102853
Open Access | Times Cited: 28
Crop genomic selection with deep learning and environmental data: A survey
Sheikh Jubair, Michael Domaratzki
Frontiers in Artificial Intelligence (2023) Vol. 5
Open Access | Times Cited: 17
The role of artificial intelligence in crop improvement
Karlene L. Negus, Xianran Li, Stephen M. Welch, et al.
Advances in agronomy (2024), pp. 1-66
Closed Access | Times Cited: 5
Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data
Germano Costa‐Neto, Leonardo Crespo‐Herrera, Nick Fradgley, et al.
G3 Genes Genomes Genetics (2022) Vol. 13, Iss. 2
Open Access | Times Cited: 24
Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America
Marco Lopez‐Cruz, Fernando Aguate, Jacob D. Washburn, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 13
Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials
Igor Kuivjogi Fernandes, Caio Canella Vieira, Kaio Olímpio das Graças Dias, et al.
Theoretical and Applied Genetics (2024) Vol. 137, Iss. 8
Open Access | Times Cited: 4
GxENet: Novel fully connected neural network based approaches to incorporate GxE for predicting wheat yield
Sheikh Jubair, Olivier Tremblay-Savard, Michael Domaratzki
Artificial Intelligence in Agriculture (2023) Vol. 8, pp. 60-76
Open Access | Times Cited: 9
Predicting Genotype × Environment × Management (G × E × M) Interactions for the Design of Crop Improvement Strategies
Mark Cooper, Carlos D. Messina, Tom Tang, et al.
Plant breeding reviews (2022), pp. 467-585
Closed Access | Times Cited: 15
High temporal resolution unoccupied aerial systems phenotyping provides unique information between flight dates
Jacob D. Washburn, Alper Adak, Aaron J. DeSalvio, et al.
The Plant Phenome Journal (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 2
Trait association and prediction through integrative k‐mer analysis
Cheng He, Jacob D. Washburn, Nathaniel Schleif, et al.
The Plant Journal (2024)
Closed Access | Times Cited: 2
Genomics combined with UAS data enhances prediction of grain yield in winter wheat
Osval A. Montesinos‐López, Andrew W. Herr, José Crossa, et al.
Frontiers in Genetics (2023) Vol. 14
Open Access | Times Cited: 7
FF-LSTM: phenotype prediction based on feature fusion
Ge Zhang, Zhou Zhang, Xuye Kou, et al.
(2024), pp. 67-67
Closed Access | Times Cited: 1
Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates
Jacob D. Washburn, José Ignacio Varela, Alencar Xavier, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 1
Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates
Jacob D. Washburn, José Ignacio Varela, Alencar Xavier, et al.
Genetics (2024)
Open Access | Times Cited: 1
Trait Association and Prediction Through Integrative K-mer Analysis
Cheng He, Jacob D. Washburn, Yangfan Hao, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2021)
Open Access | Times Cited: 7
Efficacy of plant breeding using genomic information
Osval A. Montesinos‐López, Alison R. Bentley, Carolina Saint Pierre, et al.
The Plant Genome (2023) Vol. 16, Iss. 2
Open Access | Times Cited: 2
Stacked ensembles on basis of parentage information can predict hybrid performance with an accuracy comparable to marker-based GBLUP
Philipp Georg Heilmann, Matthias Frisch, Amine Abbadi, et al.
Frontiers in Plant Science (2023) Vol. 14
Open Access | Times Cited: 2
Ensemble of best linear unbiased predictor, machine learning and deep learning models predict maize yield better than each model alone
Daniel R Kick, Jacob D. Washburn
in silico Plants (2023) Vol. 5, Iss. 2
Open Access | Times Cited: 2
Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions
Igor Kuivjogi Fernandes, Caio Canella Vieira, Kaio Olímpio das Graças Dias, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
MegaLMM improves genomic predictions in new environments using environmental covariates
Haixiao Hu, Renaud Rincent, Daniel E. Runcie
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Improvements in Prediction Performance of Ensemble Approaches for Genomic Prediction in Crop Breeding
Shunichiro Tomura, Melanie J. Wilkinson, Mark Cooper, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Closed Access
Explainable AI-guided identification of a novel protein-RNA interactive frame for selective siRNA accumulation in plants
Norio Enoki, Eriko Kuwada, Shinnosuke Matsuo, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Large-scale crop dataset and deep learning-based multi-modal fusion framework for more accurate G×E genomic prediction
Q. Zou, Shuaishuai Tai, Qi Yuan, et al.
Computers and Electronics in Agriculture (2024) Vol. 230, pp. 109833-109833
Closed Access
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