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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:

Modeling polypharmacy side effects with graph convolutional networks
Marinka Żitnik, Monica Agrawal, Jure Leskovec
Bioinformatics (2018) Vol. 34, Iss. 13, pp. i457-i466
Open Access | Times Cited: 1047

Showing 1-25 of 1047 citing articles:

Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Shengding Hu, et al.
AI Open (2020) Vol. 1, pp. 57-81
Open Access | Times Cited: 3720
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Rex Ying, Ruining He, Kaifeng Chen, et al.
(2018), pp. 974-983
Open Access | Times Cited: 3055
Deep Learning on Graphs: A Survey
Ziwei Zhang, Peng Cui, Wenwu Zhu
IEEE Transactions on Knowledge and Data Engineering (2020) Vol. 34, Iss. 1, pp. 249-270
Open Access | Times Cited: 1227
A guide to machine learning for biologists
Joe G. Greener, Shaun M. Kandathil, Lewis Moffat, et al.
Nature Reviews Molecular Cell Biology (2021) Vol. 23, Iss. 1, pp. 40-55
Open Access | Times Cited: 1218
Graph Neural Networks: A Review of Methods and Applications
Jie Zhou, Ganqu Cui, Shengding Hu, et al.
arXiv (Cornell University) (2018)
Open Access | Times Cited: 1157
Graph convolutional networks: a comprehensive review
Si Zhang, Hanghang Tong, Jiejun Xu, et al.
Computational Social Networks (2019) Vol. 6, Iss. 1
Open Access | Times Cited: 1115
A survey on deep learning and its applications
Shi Dong, Ping Wang, Khushnood Abbas
Computer Science Review (2021) Vol. 40, pp. 100379-100379
Closed Access | Times Cited: 1027
Deep learning: new computational modelling techniques for genomics
Gökçen Eraslan, Žiga Avsec, Julien Gagneur, et al.
Nature Reviews Genetics (2019) Vol. 20, Iss. 7, pp. 389-403
Closed Access | Times Cited: 945
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
Minjie Wang, Da Zheng, Zihao Ye, et al.
arXiv (Cornell University) (2019)
Open Access | Times Cited: 650
GNNExplainer: Generating Explanations for Graph Neural Networks
Rex Ying, Dylan Bourgeois, Jiaxuan You, et al.
arXiv (Cornell University) (2019)
Open Access | Times Cited: 588
Strategies for Pre-training Graph Neural Networks
Weihua Hu, Bowen Liu, Joseph Gomes, et al.
arXiv (Cornell University) (2019)
Open Access | Times Cited: 451
Network medicine framework for identifying drug-repurposing opportunities for COVID-19
Deisy Morselli Gysi, Ítalo Faria do Valle, Marinka Żitnik, et al.
Proceedings of the National Academy of Sciences (2021) Vol. 118, Iss. 19
Open Access | Times Cited: 438
Applications of machine learning to diagnosis and treatment of neurodegenerative diseases
Monika A. Myszczynska, Poojitha N. Ojamies, Alix M.B. Lacoste, et al.
Nature Reviews Neurology (2020) Vol. 16, Iss. 8, pp. 440-456
Closed Access | Times Cited: 426
Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs
Minjie Wang, Lingfan Yu, Da Zheng, et al.
arXiv (Cornell University) (2019)
Closed Access | Times Cited: 408
DySAT
Aravind Sankar, Yanhong Wu, Liang Gou, et al.
(2020), pp. 519-527
Closed Access | Times Cited: 401
Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree
Kai Sun, Richong Zhang, Samuel Mensah, et al.
(2019)
Open Access | Times Cited: 368
Integration strategies of multi-omics data for machine learning analysis
Milan Picard, Marie‐Pier Scott‐Boyer, Antoine Bodein, et al.
Computational and Structural Biotechnology Journal (2021) Vol. 19, pp. 3735-3746
Open Access | Times Cited: 349
Graph embedding on biomedical networks: methods, applications and evaluations
Xiang Yue, Zhen Wang, Jingong Huang, et al.
Bioinformatics (2019) Vol. 36, Iss. 4, pp. 1241-1251
Open Access | Times Cited: 346
MolTrans: Molecular Interaction Transformer for drug–target interaction prediction
Kexin Huang, Cao Xiao, Lucas M. Glass, et al.
Bioinformatics (2020) Vol. 37, Iss. 6, pp. 830-836
Open Access | Times Cited: 312
A survey on deep learning in medicine: Why, how and when?
Francesco Piccialli, Vittorio Di Somma, Fabio Giampaolo, et al.
Information Fusion (2020) Vol. 66, pp. 111-137
Closed Access | Times Cited: 287
Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities
Marinka Żitnik, Francis Nguyen, Bo Wang, et al.
Information Fusion (2018) Vol. 50, pp. 71-91
Open Access | Times Cited: 285
Predicting drug–disease associations through layer attention graph convolutional network
Zhouxin Yu, Feng Huang, Xiaohan Zhao, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 4
Closed Access | Times Cited: 276
A gentle introduction to deep learning for graphs
Davide Bacciu, Federico Errica, Alessio Micheli, et al.
Neural Networks (2020) Vol. 129, pp. 203-221
Open Access | Times Cited: 266
Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions
Sezen Vatansever, Avner Schlessinger, Daniel Wacker, et al.
Medicinal Research Reviews (2020) Vol. 41, Iss. 3, pp. 1427-1473
Open Access | Times Cited: 258
KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction
Xuan Lin, Zhe Quan, Zhijie Wang, et al.
(2020), pp. 2739-2745
Open Access | Times Cited: 231
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