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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:

Network-based drug sensitivity prediction
Khandakar Tanvir Ahmed, Sunho Park, Qibing Jiang, et al.
BMC Medical Genomics (2020) Vol. 13, Iss. S11
Open Access | Times Cited: 37

Showing 1-25 of 37 citing articles:

Deep learning in drug discovery: an integrative review and future challenges
Heba Askr, Enas Elgeldawi, Heba Aboul Ella, et al.
Artificial Intelligence Review (2022) Vol. 56, Iss. 7, pp. 5975-6037
Open Access | Times Cited: 183
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: 71
Omics-based deep learning approaches for lung cancer decision-making and therapeutics development
Thi-Oanh Tran, Thanh Hoa Vo, Nguyen Quoc Khanh Le
Briefings in Functional Genomics (2023) Vol. 23, Iss. 3, pp. 181-192
Closed Access | Times Cited: 30
Multi-omics data integration by generative adversarial network
Khandakar Tanvir Ahmed, Jiao Sun, Sze Cheng, et al.
Bioinformatics (2021) Vol. 38, Iss. 1, pp. 179-186
Open Access | Times Cited: 53
An overview of machine learning methods for monotherapy drug response prediction
Farzaneh Firoozbakht, Behnam Yousefi, Benno Schwikowski
Briefings in Bioinformatics (2021) Vol. 23, Iss. 1
Open Access | Times Cited: 45
Cancer omic data based explainable AI drug recommendation inference: A traceability perspective for explainability
Jianing Xi, Dan Wang, Xuebing Yang, et al.
Biomedical Signal Processing and Control (2022) Vol. 79, pp. 104144-104144
Closed Access | Times Cited: 22
CancerOmicsNet: a multi-omics network-based approach to anti-cancer drug profiling
Limeng Pu, Manali Singha, J. Ramanujam, et al.
Oncotarget (2022) Vol. 13, Iss. 1, pp. 695-706
Open Access | Times Cited: 19
Computational network biology analysis revealed COVID-19 severity markers: Molecular interplay between HLA-II with CIITA
Heewon Park, Satoru Miyano
PLoS ONE (2025) Vol. 20, Iss. 3, pp. e0319205-e0319205
Open Access
Integrating edge features and complementary attention mechanism for drug response prediction
Chuang Li, Minhui Wang, Chang Tang, et al.
Knowledge-Based Systems (2025), pp. 113508-113508
Closed Access
Identifying the serious clinical outcomes of adverse reactions to drugs by a multi-task deep learning framework
Haochen Zhao, Peng Ni, Qichang Zhao, et al.
Communications Biology (2023) Vol. 6, Iss. 1
Open Access | Times Cited: 10
Integrating spatial transcriptomics and bulk RNA-seq: predicting gene expression with enhanced resolution through graph attention networks
Sudipto Baul, Khandakar Tanvir Ahmed, Qibing Jiang, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 4
Open Access | Times Cited: 3
Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer
Kerstin Lenhof, Lea Eckhart, Lisa-Marie Rolli, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 5
Open 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: 16
Pan-Cancer Prediction of Cell-Line Drug Sensitivity Using Network-Based Methods
Maryam Pouryahya, Jung Hun Oh, James C. Mathews, et al.
International Journal of Molecular Sciences (2022) Vol. 23, Iss. 3, pp. 1074-1074
Open Access | Times Cited: 13
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
CTDN (Convolutional Temporal Based Deep‐ Neural Network): An Improvised Stacked Hybrid Computational Approach for Anticancer Drug Response Prediction
Davinder Paul Singh, Baijnath Kaushik
Computational Biology and Chemistry (2023) Vol. 105, pp. 107868-107868
Closed Access | Times Cited: 6
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
Differential Private Deep Learning Models for Analyzing Breast Cancer Omics Data
Md. Mohaiminul Islam, Noman Mohammed, Yan Wang, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 9
omicsGAT: Graph Attention Network for Cancer Subtype Analyses
Sudipto Baul, Khandakar Tanvir Ahmed, Joseph Filipek, et al.
International Journal of Molecular Sciences (2022) Vol. 23, Iss. 18, pp. 10220-10220
Open Access | Times Cited: 9
DRN- CDR: A Cancer Drug Response Prediction Model using Multi-Omics and Drug Features
K R Saranya, E. R. Vimina
Computational Biology and Chemistry (2024) Vol. 112, pp. 108175-108175
Closed Access | Times Cited: 1
Techniques for learning and transferring knowledge for microbiome-based classification and prediction: review and assessment
Jin Han, Haohong Zhang, Kang Ning
Briefings in Bioinformatics (2024) Vol. 26, Iss. 1
Open Access | Times Cited: 1
Estimation of Individual Tree Stem Biomass in an Uneven-Aged Structured Coniferous Forest Using Multispectral LiDAR Data
Nikos Georgopoulos, Ioannis Z. Gitas, Alexandra Stefanidou, et al.
Remote Sensing (2021) Vol. 13, Iss. 23, pp. 4827-4827
Open Access | Times Cited: 9
In silico model for miRNA-mediated regulatory network in cancer
Khandakar Tanvir Ahmed, Jiao Sun, William Chen, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Open Access | Times Cited: 8
Relationship between the deep features of the full-scan pathological map of mucinous gastric carcinoma and related genes based on deep learning
Li Ding, Xiaoyuan Li, Shifang Li, et al.
Heliyon (2023) Vol. 9, Iss. 3, pp. e14374-e14374
Open Access | Times Cited: 3
CancerCellTracker: a brightfield time-lapse microscopy framework for cancer drug sensitivity estimation
Qibing Jiang, Praneeth Sudalagunta, Carolina Silva, et al.
Bioinformatics (2022) Vol. 38, Iss. 16, pp. 4002-4010
Open Access | Times Cited: 5
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