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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:

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

Showing 12 citing articles:

Decoding the functional impact of the cancer genome through protein–protein interactions
Haian Fu, Xiulei Mo, Andrei A. Ivanov
Nature reviews. Cancer (2025)
Closed Access | Times Cited: 2
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
Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide
Ruth Nussinov, Bengi Ruken Yavuz, Habibe Cansu Demirel, et al.
Frontiers in Cell and Developmental Biology (2024) Vol. 12
Open Access | Times Cited: 7
Novel Computational and Artificial Intelligence Models in Cancer Research
Li Liu, Fuhai Li, Xiaoming Liu, et al.
Cancers (2025) Vol. 17, Iss. 1, pp. 116-116
Open Access
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
Network pharmacology and AI in cancer research uncovering biomarkers and therapeutic targets for RALGDS mutations
S. Mohammed Zaidh, Hariharan Thirumalai Vengateswaran, Mohammad Habeeb, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Revealing cancer driver genes through integrative transcriptomic and epigenomic analyses with Moonlight
Mona Nourbakhsh, Yuanning Zheng, Humaira Noor, et al.
PLoS Computational Biology (2025) Vol. 21, Iss. 4, pp. e1012999-e1012999
Open Access
Are Next-Generation Pathogenicity Predictors Applicable to Cancer?
Daria Ostroverkhova, Yiru Sheng, Anna R. Panchenko
Journal of Molecular Biology (2024) Vol. 436, Iss. 16, pp. 168644-168644
Open Access | Times Cited: 2
Revealing cancer driver genes through integrative transcriptomic and epigenomic analyses with Moonlight
Mona Nourbakhsh, Yuanning Zheng, Humaira Binte Noor, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Are the Next-Generation Pathogenicity Predictors Applicable to Cancer?
Daria Ostroverkhova, Yiru Sheng, Anna R. Panchenko
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Are the Next-Generation Pathogenicity Predictors Applicable to Cancer?
Daria Ostroverkhova, Yiru Sheng, Anna R. Panchenko
(2024)
Open Access
CDMPred: a tool for predicting cancer driver missense mutations with high-quality passenger mutations
Lihua Wang, Haiyang Sun, Zhenyu Yue, et al.
PeerJ (2024) Vol. 12, pp. e17991-e17991
Open Access
Graph convolution networks model identifies and quantifies gene and cancer specific transcriptome signatures of cancer driver events
Gil Ben Cohen, Adar Yaacov, Yishai Ben Zvi, et al.
Computers in Biology and Medicine (2024) Vol. 185, pp. 109491-109491
Open Access
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