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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:

GraphCDR: a graph neural network method with contrastive learning for cancer drug response prediction
Xuan Liu, Congzhi Song, Feng Huang, et al.
Briefings in Bioinformatics (2021) Vol. 23, Iss. 1
Closed Access | Times Cited: 70

Showing 1-25 of 70 citing articles:

Deep learning methods for drug response prediction in cancer: Predominant and emerging trends
Alexander Partin, Thomas Brettin, Yitan Zhu, et al.
Frontiers in Medicine (2023) Vol. 10
Open Access | Times Cited: 70
Pre-training graph neural networks for link prediction in biomedical networks
Yahui Long, Min Wu, Yong Liu, et al.
Bioinformatics (2022) Vol. 38, Iss. 8, pp. 2254-2262
Open Access | Times Cited: 53
MPCLCDA: predicting circRNA–disease associations by using automatically selected meta-path and contrastive learning
Wei Liu, Ting Tang, Xu Lu, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 4
Closed Access | Times Cited: 35
Predicting drug-induced liver injury using graph attention mechanism and molecular fingerprints
Jifeng Wang, Li Zhang, Jianqiang Sun, et al.
Methods (2023) Vol. 221, pp. 18-26
Open Access | Times Cited: 35
Towards artificial intelligence to multi-omics characterization of tumor heterogeneity in esophageal cancer
Junyu Li, Lin Li, Peimeng You, et al.
Seminars in Cancer Biology (2023) Vol. 91, pp. 35-49
Closed Access | Times Cited: 29
Multi-omics approaches for biomarker discovery in predicting the response of esophageal cancer to neoadjuvant therapy: A multidimensional perspective
Zhi Yang, Fada Guan, Lawrence F. Bronk, et al.
Pharmacology & Therapeutics (2024) Vol. 254, pp. 108591-108591
Closed Access | Times Cited: 14
Multi-way relation-enhanced hypergraph representation learning for anti-cancer drug synergy prediction
Xuan Liu, Congzhi Song, Shichao Liu, et al.
Bioinformatics (2022) Vol. 38, Iss. 20, pp. 4782-4789
Closed Access | Times Cited: 37
Predicting cancer drug response using parallel heterogeneous graph convolutional networks with neighborhood interactions
Wei Peng, Hancheng Liu, Wei Dai, et al.
Bioinformatics (2022) Vol. 38, Iss. 19, pp. 4546-4553
Closed Access | Times Cited: 33
Predicting microbe–drug associations with structure-enhanced contrastive learning and self-paced negative sampling strategy
Zhen Tian, Yue Yu, Haichuan Fang, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 2
Closed Access | Times Cited: 21
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
A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity prediction
Lea Eckhart, Kerstin Lenhof, Lisa-Marie Rolli, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 4
Open Access | Times Cited: 6
MDDI-SCL: predicting multi-type drug-drug interactions via supervised contrastive learning
Shenggeng Lin, Weizhi Chen, Gengwang Chen, et al.
Journal of Cheminformatics (2022) Vol. 14, Iss. 1
Open Access | Times Cited: 27
A subcomponent-guided deep learning method for interpretable cancer drug response prediction
Xuan Liu, Wen Zhang
PLoS Computational Biology (2023) Vol. 19, Iss. 8, pp. e1011382-e1011382
Open Access | Times Cited: 13
MGATAF: multi-channel graph attention network with adaptive fusion for cancer-drug response prediction
Dhekra Saeed, Huanlai Xing, Barakat AlBadani, et al.
BMC Bioinformatics (2025) Vol. 26, Iss. 1
Open Access
The clinical application of artificial intelligence in cancer precision treatment
Jinyu Wang, Ziyi Zeng, Zehua Li, et al.
Journal of Translational Medicine (2025) Vol. 23, Iss. 1
Open Access
ASGCL: Adaptive Sparse Mapping-based graph contrastive learning network for cancer drug response prediction
Yunyun Dong, Yuanrong Zhang, Yuhua Qian, et al.
PLoS Computational Biology (2025) Vol. 21, Iss. 1, pp. e1012748-e1012748
Open Access
A multi-task domain-adapted model to predict chemotherapy response from mutations in recurrently altered cancer genes
Aishwarya Jayagopal, Robert J. Walsh, Krishna Kumar Hariprasannan, et al.
iScience (2025) Vol. 28, Iss. 3, pp. 111992-111992
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
Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques
Yang‐Chang Wu, Ming Chen, Yufang Qin
PLoS Computational Biology (2025) Vol. 21, Iss. 3, pp. e1012905-e1012905
Open Access
Improved drug response prediction by drug target data integration via network-based profiling
Minwoo Pak, Sangseon Lee, Inyoung Sung, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 2
Closed Access | Times Cited: 9
Improving anti-cancer drug response prediction using multi-task learning on graph convolutional networks
Hancheng Liu, Wei Peng, Wei Dai, et al.
Methods (2023) Vol. 222, pp. 41-50
Closed Access | Times Cited: 9
scAMAC: self-supervised clustering of scRNA-seq data based on adaptive multi-scale autoencoder
Dayu Tan, Cheng Yang, Jing Wang, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 2
Open Access | Times Cited: 3
A Computational Framework for Predicting Novel Drug Indications Using Graph Convolutional Network With Contrastive Learning
Yuxun Luo, Wenyu Shan, Peng Li, et al.
IEEE Journal of Biomedical and Health Informatics (2024) Vol. 28, Iss. 8, pp. 4503-4511
Closed Access | Times Cited: 3
GADRP: graph convolutional networks and autoencoders for cancer drug response prediction
Hong Wang, Chong Dai, Yuqi Wen, et al.
Briefings in Bioinformatics (2022) Vol. 24, Iss. 1
Closed Access | Times Cited: 15
PANCDR: precise medicine prediction using an adversarial network for cancer drug response
Ju‐Yeon Kim, Sung‐Hye Park, Hyunju Lee
Briefings in Bioinformatics (2024) Vol. 25, Iss. 2
Open Access | Times Cited: 2
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