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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:

Machine learning methods for prediction of cancer driver genes: a survey paper
Renan Soares de Andrades, Mariana Recamonde‐Mendoza
Briefings in Bioinformatics (2022) Vol. 23, Iss. 3
Open Access | Times Cited: 27

Showing 1-25 of 27 citing articles:

Reviewing methods of deep learning for intelligent healthcare systems in genomics and biomedicine
Imran Zafar, Shakila Anwar, Faheem kanwal, et al.
Biomedical Signal Processing and Control (2023) Vol. 86, pp. 105263-105263
Closed Access | Times Cited: 33
An ensemble machine learning-based performance evaluation identifies top In-Silico pathogenicity prediction methods that best classify driver mutations in cancer
Subrata Das, Vatsal Patel, Shouvik Chakravarty, et al.
BioData Mining (2025) Vol. 18, Iss. 1
Open Access | Times Cited: 1
Multi-Network Graph Contrastive Learning for Cancer Driver Gene Identification
Wei Peng, Zhengnan Zhou, Wei Dai, et al.
IEEE Transactions on Network Science and Engineering (2024) Vol. 11, Iss. 4, pp. 3430-3440
Closed Access | Times Cited: 6
Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks
Mona Nourbakhsh, Kristine B. Degn, Astrid Saksager, et al.
Briefings in Bioinformatics (2023) Vol. 25, Iss. 2
Open Access | Times Cited: 12
A novel heterophilic graph diffusion convolutional network for identifying cancer driver genes
Tong Zhang, Shao‐Wu Zhang, Mingyu Xie, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 3
Closed Access | Times Cited: 11
Enhancing Molecular Network‐Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities
Hao Zhang, Chaohuan Lin, Y. Chen, et al.
Journal of Cellular and Molecular Medicine (2025) Vol. 29, Iss. 1
Open Access
Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning
Dazhi Lu, Yan Zheng, Xianyanling Yi, et al.
Nature Communications (2025) Vol. 16, Iss. 1
Open Access
A Web-Based Calculator to Predict Early Death Among Patients With Bone Metastasis Using Machine Learning Techniques: Development and Validation Study
M. Lei, Bing Wu, Zhicheng Zhang, et al.
Journal of Medical Internet Research (2023) Vol. 25, pp. e47590-e47590
Open Access | Times Cited: 10
Multi-omics integration analysis of GPCRs in pan-cancer to uncover inter-omics relationships and potential driver genes
Shiqi Li, Xin Chen, Jianfang Chen, et al.
Computers in Biology and Medicine (2023) Vol. 161, pp. 106988-106988
Closed Access | Times Cited: 9
Advancing cancer driver gene detection via Schur complement graph augmentation and independent subspace feature extraction
Xinqian Ma, Zhen Li, Zhenya Du, et al.
Computers in Biology and Medicine (2024) Vol. 174, pp. 108484-108484
Closed Access | Times Cited: 2
ECD-CDGI: An efficient energy-constrained diffusion model for cancer driver gene identification
Tao Wang, Linlin Zhuo, Yifan Chen, et al.
PLoS Computational Biology (2024) Vol. 20, Iss. 8, pp. e1012400-e1012400
Open Access | Times Cited: 2
The Cancermuts software package for the prioritization of missense cancer variants: a case study of AMBRA1 in melanoma
Matteo Tiberti, Luca Di Leo, Mette Vixø Vistesen, et al.
Cell Death and Disease (2022) Vol. 13, Iss. 10
Open Access | Times Cited: 9
SMG: self-supervised masked graph learning for cancer gene identification
Yan Cui, Zhikang Wang, Xiaoyu Wang, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 6
Open Access | Times Cited: 5
IDriveGenes: Cancer Driver Genes Prediction Using Machine Learning
Yasir Ali, Muhammad Sardaraz, Muhammad Tahir, et al.
IEEE Access (2023) Vol. 11, pp. 28439-28453
Open Access | Times Cited: 4
RB1 Mutations Induce Smoking‐Related Bladder Cancer by Modulating the Cytochrome P450 Pathway
Zhenguang Mao, Fang Gao, Tuo Sun, et al.
Environmental Toxicology (2024)
Closed Access | Times Cited: 1
Machine Learning Identifies a Signature of Nine Exosomal RNAs That Predicts Hepatocellular Carcinoma
Josephine Yu Yan Yap, Laura Shih Hui Goh, Ashley J. W. Lim, et al.
Cancers (2023) Vol. 15, Iss. 14, pp. 3749-3749
Open Access | Times Cited: 3
InDEP: an interpretable machine learning approach to predict cancer driver genes from multi-omics data
Hai Yang, Y. F. Liu, Yijing Yang, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 5
Closed Access | Times Cited: 3
Label reusing based graph neural network for unbalanced classification of personalized driver genes in cancer
Han-Wen Wan, Menghan Wu, Wenshan Zhao, et al.
Applied Soft Computing (2024) Vol. 159, pp. 111658-111658
Closed Access
Predicting regulatory mutations and their target genes by new computational integrative analysis: A study of follicular lymphoma
Junbai Wang, Mingyi Yang, Omer Ali, et al.
Computers in Biology and Medicine (2024) Vol. 178, pp. 108787-108787
Open Access
Drivergene.net: A Cytoscape app for the identification of driver nodes of large-scale complex networks and case studies in discovery of drug target genes
Duc-Tinh Pham, Tien-Dzung Tran
Computers in Biology and Medicine (2024) Vol. 179, pp. 108888-108888
Closed Access
Discovery of pan-cancer related genes via integrative network analysis
Yuan Zhu, Houwang Zhang, Yuanhang Yang, et al.
Briefings in Functional Genomics (2022) Vol. 21, Iss. 4, pp. 325-338
Closed Access | Times Cited: 1
Label Reusing Based Graph Neural Network for Unbalanced Classification of Personalized Driver Genes in Cancer
Han-Wen Wan, Menghan Wu, Wenshan Zhao, et al.
(2023)
Closed Access
A Web-Based Calculator to Predict Early Death Among Patients With Bone Metastasis Using Machine Learning Techniques: Development and Validation Study (Preprint)
M. Lei, Bing Wu, Zhicheng Zhang, et al.
(2023)
Open Access
Applying centrality analysis on a protein interaction network to predict colorectal cancer driver genes
Pia Saha, Gabriel Cerono
(2023)
Open Access
Identifying cooperative genes causing cancer progression with dynamic causal inference
Andrés Cifuentes Bernal, Lin Liu, Jiuyong Li, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access
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