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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:

Multimodal deep learning methods enhance genomic prediction of wheat breeding
Abelardo Montesinos‐López, Carolina Rivera, Francisco Pinto, et al.
G3 Genes Genomes Genetics (2023) Vol. 13, Iss. 5
Open Access | Times Cited: 16

Showing 16 citing articles:

Artificial intelligence in plant breeding
Muhammad Amjad Farooq, Shang Gao, Muhammad Adeel Hassan, et al.
Trends in Genetics (2024) Vol. 40, Iss. 10, pp. 891-908
Open Access | Times Cited: 15
Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software
José Crossa, Johannes W. R. Martini, Paolo Vitale, et al.
Trends in Plant Science (2025)
Open Access
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: 14
Applications of Artificial Intelligence in Wheat Breeding for Sustainable Food Security
Muhammad Ahtasham Mushtaq, Hafiz Ghulam Muhu‐Din Ahmed, Yawen Zeng
Sustainability (2024) Vol. 16, Iss. 13, pp. 5688-5688
Open Access | Times Cited: 4
Wheat genetic resources have avoided disease pandemics, improved food security, and reduced environmental footprints: A review of historical impacts and future opportunities
Julie King, Susanne Dreisigacker, Matthew Reynolds, et al.
Global Change Biology (2024) Vol. 30, Iss. 8
Open Access | Times Cited: 4
Deep learning methods improve genomic prediction of wheat breeding
Abelardo Montesinos‐López, Leonardo Crespo‐Herrera, Susanna Dreisigacker, et al.
Frontiers in Plant Science (2024) Vol. 15
Open Access | Times Cited: 3
Genetic and Genomic Pathways to Improved Wheat (Triticum aestivum L.) Yields: A Review
Zaid Chachar, Lina Fan, Sadaruddin Chachar, et al.
Agronomy (2024) Vol. 14, Iss. 6, pp. 1201-1201
Open Access | Times Cited: 1
Genomics‐based plant disease resistance prediction using machine learning
Shriprabha R. Upadhyaya, Monica F. Danilevicz, Aria Dolatabadian, et al.
Plant Pathology (2024) Vol. 73, Iss. 9, pp. 2298-2309
Open Access | Times Cited: 1
Big data and artificial intelligence‐aided crop breeding: Progress and prospects
Wanchao Zhu, Weifu Li, Hongwei Zhang, et al.
Journal of Integrative Plant Biology (2024)
Open Access | Times Cited: 1
Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers
Dennis N. Lozada, Karansher Singh Sandhu, Madhav Bhatta
BMC Genomic Data (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 4
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
ResDeepGS: A Deep Learning-Based Method for Crop Phenotype Prediction
Chaokun Yan, Jiabao Li, Qi Feng, et al.
Lecture notes in computer science (2024), pp. 470-481
Closed Access
A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding
Osval A. Montesinos‐López, Moisés Chavira-Flores, Kiasmiantini, et al.
Genetics (2024)
Closed 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
Ensemble of BLUP, Machine Learning, and Deep Learning Models Predict Maize Yield Better Than Each Model Alone
Daniel R Kick, Jacob D. Washburn
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access
Ensemble of BLUP, Machine Learning, and Deep Learning Models Predict Maize Yield Better Than Each Model Alone
Daniel R Kick, Jacob D. Washburn
Research Square (Research Square) (2023)
Open Access
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