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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:

A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer
Delora Baptista, Pedro G. Ferreira, Miguel Rocha
PLoS Computational Biology (2023) Vol. 19, Iss. 3, pp. e1010200-e1010200
Open Access | Times Cited: 15

Showing 15 citing articles:

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: 13
The recent progress of deep-learning-based in silico prediction of drug combination
Haoyang Liu, Zhiguang Fan, Jie Lin, et al.
Drug Discovery Today (2023) Vol. 28, Iss. 7, pp. 103625-103625
Closed Access | Times Cited: 16
Repurposing of the Cardiovascular Drug Statin for the Treatment of Cancers: Efficacy of Statin–Dipyridamole Combination Treatment in Melanoma Cell Lines
Nanami Irie, Kana Mizoguchi, Tomoko Warita, et al.
Biomedicines (2024) Vol. 12, Iss. 3, pp. 698-698
Open Access | Times Cited: 5
Optimized models and deep learning methods for drug response prediction in cancer treatments: a review
Wesam Ibrahim Hajim, Suhaila Zainudin, Kauthar Mohd Daud, et al.
PeerJ Computer Science (2024) Vol. 10, pp. e1903-e1903
Open Access | Times Cited: 5
A guide for active learning in synergistic drug discovery
Shuhui Wang, Alexandre Allauzen, Philippe Nghe, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Hallmarks of artificial intelligence contributions to precision oncology
Tiangen Chang, Seongyong Park, Alejandro A. Schäffer, et al.
Nature Cancer (2025)
Closed Access
A semi-supervised weighted SPCA- and convolution KAN-based model for drug response prediction
Rui Miao, Bing-Jie Zhong, Xin-Yue Mei, et al.
Frontiers in Genetics (2025) Vol. 16
Open Access
Building a unified model for drug synergy analysis powered by large language models
Tian-Yu Liu, Tinyi Chu, Xiao Luo, et al.
Nature Communications (2025) Vol. 16, Iss. 1
Open Access
Heterogeneous Expression Patterns of the Minichromosome Maintenance Complex Members in Retinoblastoma Unveil Its Clinical Significance
Junjie Tang, Yaoming Liu, Zhihui Zhang, et al.
Investigative Ophthalmology & Visual Science (2024) Vol. 65, Iss. 1, pp. 31-31
Open Access | Times Cited: 3
Mitophagy-promoting agents and their ability to promote healthy-aging
Vijigisha Srivastava, Einav Gross
Biochemical Society Transactions (2023) Vol. 51, Iss. 5, pp. 1811-1846
Open Access | Times Cited: 7
MMSyn: A New Multimodal Deep Learning Framework for Enhanced Prediction of Synergistic Drug Combinations
Yu Pang, Yihao Chen, Mujie Lin, et al.
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 9, pp. 3689-3705
Closed Access | Times Cited: 1
BAITSAO: Building A Foundation Model for Drug Synergy Analysis Powered by Language Models
Tianyu Liu, Tinyi Chu, Xiao Luo, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Up to the Herculean Task of Tackling Cancer Therapy Resistance
Kostas A. Papavassiliou, Athanasios G. Papavassiliou
Cancers (2024) Vol. 16, Iss. 10, pp. 1826-1826
Open Access
Optimizing drug synergy prediction through categorical embeddings in Deep Neural Networks
Manuel González Lastre, Pablo González de Prado Salas, Raúl Guantes
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Molecular Fingerprints and Pharmacophores for Computational Drug Repurposing
Lina Rozano, Muhammad Irfan Abdul Jalal, Nur Alyaa Afifah Md Shahri, et al.
Elsevier eBooks (2024)
Closed Access
Leveraging big data and artificial intelligence for smarter trials in myeloproliferative neoplasms
Judith M. Bliss, Spencer Krichevsky, Joseph M. Scandura, et al.
Frontiers in Hematology (2024) Vol. 3
Open Access
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