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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:

Reinforcement learning strategies in cancer chemotherapy treatments: A review
Chan‐Yun Yang, Chamani Shiranthika, Chung-Yih Wang, et al.
Computer Methods and Programs in Biomedicine (2022) Vol. 229, pp. 107280-107280
Closed Access | Times Cited: 38

Showing 1-25 of 38 citing articles:

Dynamic maintenance scheduling approach under uncertainty: Comparison between reinforcement learning, genetic algorithm simheuristic, dispatching rules
Marcelo Luis Ruiz Rodríguez, Sylvain Kubler, Jérémy Robert, et al.
Expert Systems with Applications (2024) Vol. 248, pp. 123404-123404
Open Access | Times Cited: 10
Model‐Informed Reinforcement Learning for Enabling Precision Dosing Via Adaptive Dosing
Elena M. Tosca, Alessandro De Carlo, Davide Ronchi, et al.
Clinical Pharmacology & Therapeutics (2024) Vol. 116, Iss. 3, pp. 619-636
Open Access | Times Cited: 8
Image-based predictive modelling frameworks for personalised drug delivery in cancer therapy
Ajay Bhandari, Boram Gu, Farshad Moradi Kashkooli, et al.
Journal of Controlled Release (2024) Vol. 370, pp. 721-746
Open Access | Times Cited: 7
Graphene-Based Photodynamic Therapy and Overcoming Cancer Resistance Mechanisms: A Comprehensive Review
Hanna Dilenko, Kateřina Tománková, Lucie Válková, et al.
International Journal of Nanomedicine (2024) Vol. Volume 19, pp. 5637-5680
Open Access | Times Cited: 7
Optimization of chemotherapy regimens using mathematical programming
Konstantin Bräutigam
Computers & Industrial Engineering (2024) Vol. 191, pp. 110078-110078
Open Access | Times Cited: 6
Malignant mesothelioma tumours: molecular pathogenesis, diagnosis, and therapies accompanying clinical studies
Ram Kumar Sahu, Sakina Ruhi, Ashok Kumar Jeppu, et al.
Frontiers in Oncology (2023) Vol. 13
Open Access | Times Cited: 16
Role of Functionalized Peptides in Nanomedicine for Effective Cancer Therapy
Kibeom Kim, Myoung‐Hwan Park
Biomedicines (2024) Vol. 12, Iss. 1, pp. 202-202
Open Access | Times Cited: 5
Multi-Objective Deep Reinforcement Learning for Personalized Dose Optimization Based on Multi-Indicator Experience Replay
Lin Huo, Yuepeng Tang
Applied Sciences (2022) Vol. 13, Iss. 1, pp. 325-325
Open Access | Times Cited: 20
Deep reinforcement learning-based control of chemo-drug dose in cancer treatment
Hoda Mashayekhi, Mostafa Nazari, Fatemeh Jafarinejad, et al.
Computer Methods and Programs in Biomedicine (2023) Vol. 243, pp. 107884-107884
Closed Access | Times Cited: 12
Inverse design of nanophotonic devices enabled by optimization algorithms and deep learning: recent achievements and future prospects
Junhyeong Kim, Jae‐Yong Kim, Jungmin Kim, et al.
Nanophotonics (2025)
Open Access
Intelligent terminal SMC for non-linear chemotherapy using Markovian-based deep on-policy learning
Zahra Rasooli, Juliang Yin, Mostafa Taheri
Expert Systems with Applications (2025), pp. 126988-126988
Closed Access
Hybrid Systems of Gels and Nanoparticles for Cancer Therapy: Advances in Multifunctional Therapeutic Platforms
Kibeom Kim
Gels (2025) Vol. 11, Iss. 3, pp. 170-170
Open Access
Hallmarks of artificial intelligence contributions to precision oncology
Tiangen Chang, Seongyong Park, Alejandro A. Schäffer, et al.
Nature Cancer (2025)
Closed Access
Reinforcement Learning in Personalized Medicine: A Comprehensive Review of Treatment Optimization Strategies
K Banumathi, Latha Venkatesan, Lizy Sonia Benjamin, et al.
Cureus (2025)
Open Access
Analysis and optimization control of lung cancer treatment based on surgical resection and immune cell therapy
Fang Wen, Xuewen Tan, Yan–Bin Feng
Networks and Heterogeneous Media (2025) Vol. 20, Iss. 2, pp. 356-386
Open Access
“Improving Sample Efficiency and Exploration in Upside-Down Reinforcement Learning”
Mohammadreza Nakhaei, Reza Askari Moghadam
Journal of Information and Intelligence (2025)
Open Access
Machine Learning Methods for Precision Dosing in Anticancer Drug Therapy: A Scoping Review
Olga Teplytska, Moritz Ernst, Luca Marie Koltermann, et al.
Clinical Pharmacokinetics (2024) Vol. 63, Iss. 9, pp. 1221-1237
Open Access | Times Cited: 3
A Primer on Reinforcement Learning in Medicine for Clinicians
Pushkala Jayaraman, Jacob Desman, Moein Sabounchi, et al.
npj Digital Medicine (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 3
Just how transformative will AI/ML be for immuno-oncology?
Daniel Bottomly, Shannon K. McWeeney
Journal for ImmunoTherapy of Cancer (2024) Vol. 12, Iss. 3, pp. e007841-e007841
Open Access | Times Cited: 2
Harnessing Machine Learning Potential for Personalised Drug Design and Overcoming Drug Resistance
Mohammed Ageeli Hakami
Journal of drug targeting (2024) Vol. 32, Iss. 8, pp. 918-930
Closed Access | Times Cited: 2
Advances on Delivery System of Active Ingredients of Dried Toad Skin and Toad Venom
Dan Zhang, Bingtao Zhai, Jing Sun, et al.
International Journal of Nanomedicine (2024) Vol. Volume 19, pp. 7273-7305
Open Access | Times Cited: 2
Advances in Multifunctional Chemotherapeutic Prodrugs for Near‐infrared Fluorescence Imaging‐guided Therapy
Jiayu Zeng, Minhui Liu, Songjiao Li, et al.
ChemBioChem (2023) Vol. 24, Iss. 12
Closed Access | Times Cited: 6
Multi-objective optimization of cancer treatment using the multi-objective gray wolf optimizer (MOGWO)
Linkai Chen, Honghui Fan, Hongjin Zhu
Multiscale and Multidisciplinary Modeling Experiments and Design (2023) Vol. 7, Iss. 3, pp. 1857-1866
Closed Access | Times Cited: 4
Enhancing Medical Training through Learning from Mistakes by Interacting with an Ill-trained Reinforcement Learning Agent
Yasar C. Kakdas, Sinan Kockara, Tansel Halic, et al.
IEEE Transactions on Learning Technologies (2024) Vol. 17, pp. 1248-1260
Closed Access | Times Cited: 1
A Prediction Nomogram for No-Reflow in Acute Myocardial Infarction Patients after Primary Percutaneous Coronary Intervention
Bowen Lou, Kejia Kan, Hui Liu, et al.
Reviews in Cardiovascular Medicine (2024) Vol. 25, Iss. 5
Open Access | Times Cited: 1
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