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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:

A cross-study analysis of drug response prediction in cancer cell lines
Fangfang Xia, Jonathan Allen, Prasanna Balaprakash, et al.
Briefings in Bioinformatics (2021) Vol. 23, Iss. 1
Open Access | Times Cited: 61

Showing 1-25 of 61 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
Advances in AI and machine learning for predictive medicine
Alok Sharma, Artem Lysenko, Shangru Jia, et al.
Journal of Human Genetics (2024) Vol. 69, Iss. 10, pp. 487-497
Open Access | Times Cited: 33
Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation
Eric Stahlberg, Mohamed H. Abdel‐Rahman, Boris Aguilar, et al.
Frontiers in Digital Health (2022) Vol. 4
Open Access | Times Cited: 57
Learning and actioning general principles of cancer cell drug sensitivity
Francesco Carli, Pierluigi Di Chiaro, Mariangela Morelli, et al.
Nature Communications (2025) Vol. 16, Iss. 1
Open Access | Times Cited: 1
Drug sensitivity prediction from cell line-based pharmacogenomics data: guidelines for developing machine learning models
Hossein Sharifi-Noghabi, Soheil Jahangiri-Tazehkand, Petr Smirnov, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Open Access | Times Cited: 41
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
How much can deep learning improve prediction of the responses to drugs in cancer cell lines?
Yurui Chen, Louxin Zhang
Briefings in Bioinformatics (2021) Vol. 23, Iss. 1
Closed Access | Times Cited: 34
Predicting drug response from single-cell expression profiles of tumours
Simona Pellecchia, Gaetano Viscido, Melania Franchini, et al.
BMC Medicine (2023) Vol. 21, Iss. 1
Open Access | Times Cited: 15
A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer
Delora Baptista, Pedro G. Ferreira, Miguel Rocha
PLoS Computational Biology (2023) Vol. 19, Iss. 3, pp. e1010200-e1010200
Open Access | Times Cited: 14
Optimized models and deep learning methods for drug response prediction in cancer treatments: a review
Wesam Ibrahim Hajim, Suhaila Zainudin, Kauthar Mohd Daud, et al.
PeerJ Computer Science (2024) Vol. 10, pp. e1903-e1903
Open Access | Times Cited: 5
Phoenix dactylifera L. (date palm) fruit extracts and fractions exhibit anti-proliferative activity against human pancreatic cancer cell lines
Reem A Al Alawi, Jörg D. Hoheisel, Mohamed Saiel Saeed Alhamdani, et al.
Heliyon (2025), pp. e42274-e42274
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
An NLP-based technique to extract meaningful features from drug SMILES
Rahul Sharma, Ehsan Saghapour, Jake Y. Chen
iScience (2024) Vol. 27, Iss. 3, pp. 109127-109127
Open Access | Times Cited: 4
Automatic detection of cell-cycle stages using recurrent neural networks
Abin Jose, Rijo Roy, Daniel Moreno-Andrés, et al.
PLoS ONE (2024) Vol. 19, Iss. 3, pp. e0297356-e0297356
Open Access | Times Cited: 4
Improving drug response prediction via integrating gene relationships with deep learning
Pengyong Li, Zhengxiang Jiang, Tianxiao Liu, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 3
Open Access | Times Cited: 4
Out-of-distribution generalization from labelled and unlabelled gene expression data for drug response prediction
Hossein Sharifi-Noghabi, Parsa Alamzadeh Harjandi, Olga Zolotareva, et al.
Nature Machine Intelligence (2021) Vol. 3, Iss. 11, pp. 962-972
Closed Access | Times Cited: 23
A systematic assessment of deep learning methods for drug response prediction: from in vitro to clinical applications
Bihan Shen, Fangyoumin Feng, Kunshi Li, et al.
Briefings in Bioinformatics (2022) Vol. 24, Iss. 1
Closed Access | Times Cited: 17
DeepDRA: Drug repurposing using multi-omics data integration with autoencoders
Taha Mohammadzadeh-Vardin, Amin Ghareyazi, Ali Gharizadeh, et al.
PLoS ONE (2024) Vol. 19, Iss. 7, pp. e0307649-e0307649
Open Access | Times Cited: 3
Multitask Learning on Graph Convolutional Residual Neural Networks for Screening of Multitarget Anticancer Compounds
Thanh‐Hoang Nguyen‐Vo, T. T. Trang, Binh P. Nguyen
Journal of Chemical Information and Modeling (2024) Vol. 64, Iss. 18, pp. 6957-6968
Closed Access | Times Cited: 3
Knowledge graphs facilitate prediction of drug response for acute myeloid leukemia
Guangrong Qin, Yue Zhang, Jeffrey W. Tyner, et al.
iScience (2024) Vol. 27, Iss. 9, pp. 110755-110755
Open Access | Times Cited: 3
Scaffold Splits Overestimate Virtual Screening Performance
Qianrong Guo, Saiveth Hernández-Hernández, Pedro J. Ballester
Lecture notes in computer science (2024), pp. 58-72
Closed Access | Times Cited: 3
PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis
Nikta Feizi, Sisira Kadambat Nair, Petr Smirnov, et al.
Nucleic Acids Research (2021) Vol. 50, Iss. D1, pp. D1348-D1357
Open Access | Times Cited: 22
Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images
Alexander Partin, Thomas Brettin, Yitan Zhu, et al.
Frontiers in Medicine (2023) Vol. 10
Open Access | Times Cited: 7
DWUT-MLP: Classification of anticancer drug response using various feature selection and classification techniques
Davinder Paul Singh, Abhishek Gupta, Baijnath Kaushik
Chemometrics and Intelligent Laboratory Systems (2022) Vol. 225, pp. 104562-104562
Closed Access | Times Cited: 9
Utilization of Cancer Cell Line Screening to Elucidate the Anticancer Activity and Biological Pathways Related to the Ruthenium-Based Therapeutic BOLD-100
Brian Park, Paromita Raha, Jim Pankovich, et al.
Cancers (2022) Vol. 15, Iss. 1, pp. 28-28
Open Access | Times Cited: 9
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