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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:

Anticancer Drug Response Prediction in Cell Lines Using Weighted Graph Regularized Matrix Factorization
Na‐Na Guan, Yan Zhao, Chun-Chun Wang, et al.
Molecular Therapy — Nucleic Acids (2019) Vol. 17, pp. 164-174
Open Access | Times Cited: 86

Showing 1-25 of 86 citing articles:

An Improved Anticancer Drug-Response Prediction Based on an Ensemble Method Integrating Matrix Completion and Ridge Regression
Chuanying Liu, Wei Dong, Ju Xiang, et al.
Molecular Therapy — Nucleic Acids (2020) Vol. 21, pp. 676-686
Open Access | Times Cited: 80
Machine learning methods, databases and tools for drug combination prediction
Lianlian Wu, Yuqi Wen, Dongjin Leng, et al.
Briefings in Bioinformatics (2021) Vol. 23, Iss. 1
Open Access | Times Cited: 77
Ensemble transfer learning for the prediction of anti-cancer drug response
Yitan Zhu, Thomas Brettin, Yvonne A. Evrard, et al.
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 76
Predicting Drug Response Based on Multi-Omics Fusion and Graph Convolution
Wei Peng, Tielin Chen, Wei Dai
IEEE Journal of Biomedical and Health Informatics (2021) Vol. 26, Iss. 3, pp. 1384-1393
Closed Access | Times Cited: 67
Improving drug response prediction by integrating multiple data sources: matrix factorization, kernel and network-based approaches
Betül Güvenç Paltun, Hiroshi Mamitsuka, Samuel Kaski
Briefings in Bioinformatics (2019) Vol. 22, Iss. 1, pp. 346-359
Open Access | Times Cited: 71
Predicting tumor response to drugs based on gene-expression biomarkers of sensitivity learned from cancer cell lines
Yuanyuan Li, David M. Umbach, J.M. Krahn, et al.
BMC Genomics (2021) Vol. 22, Iss. 1
Open Access | Times Cited: 46
SWnet: a deep learning model for drug response prediction from cancer genomic signatures and compound chemical structures
Zhaorui Zuo, Penglei Wang, Xiaowei Chen, et al.
BMC Bioinformatics (2021) Vol. 22, Iss. 1
Open Access | Times Cited: 45
An overview of machine learning methods for monotherapy drug response prediction
Farzaneh Firoozbakht, Behnam Yousefi, Benno Schwikowski
Briefings in Bioinformatics (2021) Vol. 23, Iss. 1
Open Access | Times Cited: 43
iGRLCDA: identifying circRNA–disease association based on graph representation learning
Han-Yuan Zhang, Lei Wang, Zhu‐Hong You, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 3
Closed Access | Times Cited: 36
Identification of Drug-Side Effect Association Via Multi-View Semi-Supervised Sparse Model
Yijie Ding, Fei Guo, Prayag Tiwari, et al.
IEEE Transactions on Artificial Intelligence (2023) Vol. 5, Iss. 5, pp. 2151-2162
Closed Access | Times Cited: 20
Improving drug response prediction based on two-space graph convolution
Wei Peng, Tielin Chen, Hancheng Liu, et al.
Computers in Biology and Medicine (2023) Vol. 158, pp. 106859-106859
Closed Access | Times Cited: 17
TGSA: protein–protein association-based twin graph neural networks for drug response prediction with similarity augmentation
Yiheng Zhu, Zhenqiu Ouyang, Wenbo Chen, et al.
Bioinformatics (2021) Vol. 38, Iss. 2, pp. 461-468
Closed Access | Times Cited: 33
Graph Transformer for Drug Response Prediction
Thang Chu, Thuy Trang Nguyen, Bùi Dương Hải, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2022) Vol. 20, Iss. 2, pp. 1065-1072
Open Access | Times Cited: 27
DRPreter: Interpretable Anticancer Drug Response Prediction Using Knowledge-Guided Graph Neural Networks and Transformer
Jihye Shin, Yinhua Piao, Dongmin Bang, et al.
International Journal of Molecular Sciences (2022) Vol. 23, Iss. 22, pp. 13919-13919
Open Access | Times Cited: 27
Drug Repositioning with GraphSAGE and Clustering Constraints Based on Drug and Disease Networks
Yuchen Zhang, Xiujuan Lei, Yi Pan, et al.
Frontiers in Pharmacology (2022) Vol. 13
Open Access | Times Cited: 22
CDPMF-DDA: contrastive deep probabilistic matrix factorization for drug-disease association prediction
Xianfang Tang, Yawen Hou, Yajie Meng, et al.
BMC Bioinformatics (2025) Vol. 26, Iss. 1
Open Access
SensitiveCancerGPT: Leveraging Generative Large Language Model on Structured Omics Data to Optimize Drug Sensitivity Prediction
Shaika Chowdhury, Sivaraman Rajaganapathy, Lichao Sun, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2025)
Open Access
DRExplainer: Quantifiable interpretability in drug response prediction with directed graph convolutional network
Haoyuan Shi, Tao Xu, Xiaodi Li, et al.
Artificial Intelligence in Medicine (2025) Vol. 163, pp. 103101-103101
Open Access
A Comprehensive Review of Various Machine Learning and Deep Learning Models for Anti-Cancer Drug Response Prediction: Comparative Analysis With Existing State of the Art Methods
Davinder Paul Singh, Prabhjot Kour, Tathagat Banerjee, et al.
Archives of Computational Methods in Engineering (2025)
Closed Access
Leveraging TCGA gene expression data to build predictive models for cancer drug response
Evan A. Clayton, Toyya A. Pujol, John F. McDonald, et al.
BMC Bioinformatics (2020) Vol. 21, Iss. S14
Open Access | Times Cited: 37
Q-omics: Smart Software for Assisting Oncology and Cancer Research
Jieun Lee, Young‐Ju Kim, Seonghee Jin, et al.
Molecules and Cells (2021) Vol. 44, Iss. 11, pp. 843-850
Open Access | Times Cited: 28
In Vivo Modeling of Human Breast Cancer Using Cell Line and Patient-Derived Xenografts
Eric P. Souto, Lacey E. Dobrolecki, Hugo Villanueva, et al.
Journal of Mammary Gland Biology and Neoplasia (2022) Vol. 27, Iss. 2, pp. 211-230
Open Access | Times Cited: 19
Reliable anti-cancer drug sensitivity prediction and prioritization
Kerstin Lenhof, Lea Eckhart, Lisa-Marie Rolli, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 4
Adaptive-weighted federated graph convolutional networks with multi-sensor data fusion for drug response prediction
Yu Hui, Qingyong Wang, Xiaobo Zhou
Information Fusion (2025), pp. 103147-103147
Closed Access
Computational drug repositioning using similarity constrained weight regularization matrix factorization: A case of COVID‐19
Junlin Xu, Yajie Meng, Lihong Peng, et al.
Journal of Cellular and Molecular Medicine (2022) Vol. 26, Iss. 13, pp. 3772-3782
Open Access | Times Cited: 18
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