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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:

Biologically informed deep neural network for prostate cancer discovery
Haitham Elmarakeby, Justin H. Hwang, Rand Arafeh, et al.
Nature (2021) Vol. 598, Iss. 7880, pp. 348-352
Open Access | Times Cited: 266

Showing 1-25 of 266 citing articles:

Scientific discovery in the age of artificial intelligence
Hanchen Wang, Tianfan Fu, Yuanqi Du, et al.
Nature (2023) Vol. 620, Iss. 7972, pp. 47-60
Closed Access | Times Cited: 702
Obtaining genetics insights from deep learning via explainable artificial intelligence
Gherman Novakovsky, Nick Dexter, Maxwell W. Libbrecht, et al.
Nature Reviews Genetics (2022) Vol. 24, Iss. 2, pp. 125-137
Closed Access | Times Cited: 220
Artificial intelligence-powered electronic skin
Changhao Xu, Samuel A. Solomon, Wei Gao
Nature Machine Intelligence (2023) Vol. 5, Iss. 12, pp. 1344-1355
Open Access | Times Cited: 91
Biologically informed deep learning to query gene programs in single-cell atlases
Mohammad Lotfollahi, Sergei Rybakov, Karin Hrovatin, et al.
Nature Cell Biology (2023)
Open Access | Times Cited: 57
Intelligent optoelectronic processor for orbital angular momentum spectrum measurement
Hao Wang, Ziyu Zhan, Futai Hu, et al.
PhotoniX (2023) Vol. 4, Iss. 1
Open Access | Times Cited: 56
Deep learning supported discovery of biomarkers for clinical prognosis of liver cancer
Junhao Liang, Weisheng Zhang, Jianghui Yang, et al.
Nature Machine Intelligence (2023) Vol. 5, Iss. 4, pp. 408-420
Open Access | Times Cited: 48
Advancing Computational Toxicology by Interpretable Machine Learning
Xuelian Jia, Tong Wang, Hao Zhu
Environmental Science & Technology (2023) Vol. 57, Iss. 46, pp. 17690-17706
Open Access | Times Cited: 47
An overview and a roadmap for artificial intelligence in hematology and oncology
Wiebke Rösler, Michael Altenbuchinger, Bettina Baeßler, et al.
Journal of Cancer Research and Clinical Oncology (2023) Vol. 149, Iss. 10, pp. 7997-8006
Open Access | Times Cited: 45
Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions
William Lotter, Michael J. Hassett, Nikolaus Schultz, et al.
Cancer Discovery (2024) Vol. 14, Iss. 5, pp. 711-726
Open Access | Times Cited: 32
Deep learning in cancer genomics and histopathology
Michaela Unger, Jakob Nikolas Kather
Genome Medicine (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 24
MUMA: A Multi-Omics Meta-Learning Algorithm for Data Interpretation and Classification
Haihui Huang, Jun Shu, Yong Liang
IEEE Journal of Biomedical and Health Informatics (2024) Vol. 28, Iss. 4, pp. 2428-2436
Closed Access | Times Cited: 20
Current and future directions in network biology
Marinka Žitnik, Michelle M. Li, A. V. Wells, et al.
Bioinformatics Advances (2024) Vol. 4, Iss. 1
Open Access | Times Cited: 19
Biologically informed deep learning for explainable epigenetic clocks
Aurél Prósz, Orsolya Pipek, Judit Börcsök, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 17
A deep learning model of tumor cell architecture elucidates response and resistance to CDK4/6 inhibitors
Sungjoon Park, Erica Silva, Akshat Singhal, et al.
Nature Cancer (2024) Vol. 5, Iss. 7, pp. 996-1009
Open Access | Times Cited: 17
Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction
Guillaume Jaume, Anurag Vaidya, Richard J. Chen, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024) Vol. 9, pp. 11579-11590
Closed Access | Times Cited: 17
Applying interpretable machine learning in computational biology—pitfalls, recommendations and opportunities for new developments
Valerie Chen, Muyu Yang, Wenbo Cui, et al.
Nature Methods (2024) Vol. 21, Iss. 8, pp. 1454-1461
Closed Access | Times Cited: 15
Prediction of checkpoint inhibitor immunotherapy efficacy for cancer using routine blood tests and clinical data
Seong‐Keun Yoo, Conall Fitzgerald, Byuri Angela Cho, et al.
Nature Medicine (2025)
Open Access | Times Cited: 4
Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology
Chengyue Wu, Guillermo Lorenzo, David A. Hormuth, et al.
Biophysics Reviews (2022) Vol. 3, Iss. 2
Open Access | Times Cited: 67
MOMA: a multi-task attention learning algorithm for multi-omics data interpretation and classification
Sehwan Moon, Hyunju Lee
Bioinformatics (2022) Vol. 38, Iss. 8, pp. 2287-2296
Open Access | Times Cited: 51
Machine learning may accelerate the recognition and control of microplastic pollution: Future prospects
Fubo Yu, Xiangang Hu
Journal of Hazardous Materials (2022) Vol. 432, pp. 128730-128730
Closed Access | Times Cited: 43
A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data
Magdalena Wysocka, Oskar Wysocki, Marie Zufferey, et al.
BMC Bioinformatics (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 30
Interpreting biologically informed neural networks for enhanced proteomic biomarker discovery and pathway analysis
Erik Hartman, Aaron Scott, Christofer Karlsson, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 29
Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy
Zhe Zhang, Xiawei Wei
Seminars in Cancer Biology (2023) Vol. 90, pp. 57-72
Closed Access | Times Cited: 24
Cancer Mutations Converge on a Collection of Protein Assemblies to Predict Resistance to Replication Stress
Xiaoyu Zhao, Akshat Singhal, Sungjoon Park, et al.
Cancer Discovery (2024) Vol. 14, Iss. 3, pp. 508-523
Open Access | Times Cited: 12
A review of mechanistic learning in mathematical oncology
John Metzcar, Catherine R. Jutzeler, Paul Macklin, et al.
Frontiers in Immunology (2024) Vol. 15
Open Access | Times Cited: 12
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