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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:

Machine learning model to predict oncologic outcomes for drugs in randomized clinical trials
Alexander Schperberg, Amélie Boichard, Igor F. Tsigelny, et al.
International Journal of Cancer (2020) Vol. 147, Iss. 9, pp. 2537-2549
Open Access | Times Cited: 19

Showing 19 citing articles:

Artificial Intelligence Applied to clinical trials: opportunities and challenges
Scott Askin, Denis Burkhalter, Gilda Calado, et al.
Health and Technology (2023) Vol. 13, Iss. 2, pp. 203-213
Open Access | Times Cited: 138
Artificial intelligence in pancreatic cancer
Bowen Huang, Haoran Huang, Shuting Zhang, et al.
Theranostics (2022) Vol. 12, Iss. 16, pp. 6931-6954
Open Access | Times Cited: 72
Are Deep Learning Structural Models Sufficiently Accurate for Virtual Screening? Application of Docking Algorithms to AlphaFold2 Predicted Structures
Anna M. Díaz-Rovira, Helena Martín, Thijs Beuming, et al.
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 6, pp. 1668-1674
Open Access | Times Cited: 62
The Role of Artificial Intelligence on Tumor Boards: Perspectives from Surgeons, Medical Oncologists and Radiation Oncologists
Valerio Nardone, Federica Marmorino, Marco Maria Germani, et al.
Current Oncology (2024) Vol. 31, Iss. 9, pp. 4984-5007
Open Access | Times Cited: 9
Smart science: How artificial intelligence is revolutionizing pharmaceutical medicine
B V Swapna, Shibani Shetty, Manjunath Shetty, et al.
Acta Marisiensis. Seria Medica (2024) Vol. 70, Iss. 1, pp. 8-15
Open Access | Times Cited: 4
Implementation of artificial intelligence approaches in oncology clinical trials: A systematic review
M. SAADY, Mahmoud Eissa, Ahmed S. Yacoub, et al.
Artificial Intelligence in Medicine (2025) Vol. 161, pp. 103066-103066
Closed Access
Advancing pancreatic cancer management: the role of artificial intelligence in diagnosis and therapy
Dilber Uzun Ozsahin, Natacha Usanase, İlker Özşahin
Beni-Suef University Journal of Basic and Applied Sciences (2025) Vol. 14, Iss. 1
Open Access
TDC-2: Multimodal Foundation for Therapeutic Science
Alejandro Velez-Arce, Xiang Lin, Michelle M. Li, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 3
Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning
Milad Mousavi, Mahsa Dehghan Manshadi, M. Soltani, et al.
Computers in Biology and Medicine (2022) Vol. 146, pp. 105511-105511
Open Access | Times Cited: 14
A systematic literature review for the prediction of anticancer drug response using various machine‐learning and deep‐learning techniques
Davinder Paul Singh, Baijnath Kaushik
Chemical Biology & Drug Design (2022) Vol. 101, Iss. 1, pp. 175-194
Closed Access | Times Cited: 13
Predicting Phase 1 Lymphoma Clinical Trial Durations Using Machine Learning: An In-Depth Analysis and Broad Application Insights
Bowen Long, Shao-Wen Lai, Jiawen Wu, et al.
Clinics and Practice (2023) Vol. 14, Iss. 1, pp. 69-88
Open Access | Times Cited: 4
Applying Artificial Intelligence in Pediatric Clinical Trials: Potential Impacts and Obstacles
Henry P. Foote, Michael Cohen‐Wolkowiez, Christopher J. Lindsell, et al.
The Journal of Pediatric Pharmacology and Therapeutics (2024) Vol. 29, Iss. 3, pp. 336-340
Closed Access | Times Cited: 1
Machine learning Algorithms and Biomarkers Identification for Pancreatic cancer Diagnosis Using Multi-Omics Data Integration
Arian Karimi Rouzbahani, Ghazaleh Khalili‐Tanha, Yasamin Rajabloo, et al.
Pathology - Research and Practice (2024) Vol. 263, pp. 155602-155602
Closed Access | Times Cited: 1
Are Deep Learning Structural Models Sufficiently Accurate for Virtual Screening? Application of Docking Algorithms to AlphaFold2 Predicted Structures
Anna M. Díaz-Rovira, Helena Martín, Thijs Beuming, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access | Times Cited: 6
Correlation Between Early Trends of a Prognostic Biomarker and Overall Survival in Non–Small-Cell Lung Cancer Clinical Trials
Hugo Loureiro, T Kolben, Astrid Kiermaier, et al.
JCO Clinical Cancer Informatics (2023), Iss. 7
Open Access | Times Cited: 2
Taming Surgical Inflammation: should steroids be an essential component of microcirculatory care to reduce postoperative complications?
Bernhard Riedel, Chad Oughton, Henrik Kehlet, et al.
ANZ Journal of Surgery (2024)
Open Access
Predicting Phase 1 Lymphoma Clinical Trial Durations Using Machine Learning: An In-depth Analysis and Broad Application Insights
Bowen Long, Shao-Wen Lai, Jiawen Wu, et al.
(2023)
Open Access | Times Cited: 1
Development and interpretation of a clinicopathological-based model for the identification of microsatellite instability in Colorectal Cancer
Zhenxing Jiang, Yinghao Cao, Lizhao Yan, et al.
Research Square (Research Square) (2022)
Closed Access | Times Cited: 1
SDH mutations, as potential predictor of chemotherapy prognosis in small cell lung cancer patients
Ran Zeng, Xiaoyun Fan, Jin Yang, et al.
Discover Oncology (2023) Vol. 14, Iss. 1
Open Access
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