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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 drug-target interaction based on protein features using undersampling and feature selection techniques with boosting
Saifuddin Mahmud, Wenyu Chen, Meng Han, et al.
Analytical Biochemistry (2019) Vol. 589, pp. 113507-113507
Closed Access | Times Cited: 71

Showing 1-25 of 71 citing articles:

iLearnPlus:a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization
Zhen Chen, Pei Zhao, Chen Li, et al.
Nucleic Acids Research (2021) Vol. 49, Iss. 10, pp. e60-e60
Open Access | Times Cited: 195
SuperPred 3.0: drug classification and target prediction—a machine learning approach
Kathleen A. Gallo, Andrean Goede, Robert Preißner, et al.
Nucleic Acids Research (2022) Vol. 50, Iss. W1, pp. W726-W731
Open Access | Times Cited: 132
A review on compound-protein interaction prediction methods: Data, format, representation and model
Sangsoo Lim, Yijingxiu Lu, Chang Yun Cho, et al.
Computational and Structural Biotechnology Journal (2021) Vol. 19, pp. 1541-1556
Open Access | Times Cited: 90
Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients
Saifuddin Mahmud, Md. Al-Mustanjid, Farzana Akter, et al.
Briefings in Bioinformatics (2021)
Open Access | Times Cited: 87
PreDTIs: prediction of drug–target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques
Saifuddin Mahmud, Wenyu Chen, Yongsheng Liu, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 5
Open Access | Times Cited: 58
iFeatureOmega:an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets
Zhen Chen, Xuhan Liu, Pei Zhao, et al.
Nucleic Acids Research (2022) Vol. 50, Iss. W1, pp. W434-W447
Open Access | Times Cited: 57
DTI-HETA: prediction of drug–target interactions based on GCN and GAT on heterogeneous graph
Kanghao Shao, Yunhao Zhang, Yuqi Wen, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 3
Closed Access | Times Cited: 49
A review of deep learning methods for ligand based drug virtual screening
Hongjie Wu, Junkai Liu, Runhua Zhang, et al.
Fundamental Research (2024) Vol. 4, Iss. 4, pp. 715-737
Open Access | Times Cited: 13
Application of Machine Learning for Drug–Target Interaction Prediction
Lei Xu, Xiaoqing Ru, Rong Song
Frontiers in Genetics (2021) Vol. 12
Open Access | Times Cited: 50
Improved prediction of drug-drug interactions using ensemble deep neural networks
Thanh Hoa Vo, Ngan Nguyen, Nguyen Quoc Khanh Le
Medicine in Drug Discovery (2022) Vol. 17, pp. 100149-100149
Open Access | Times Cited: 37
Detection of molecular signatures and pathways shared in inflammatory bowel disease and colorectal cancer: A bioinformatics and systems biology approach
Md. Al-Mustanjid, Saifuddin Mahmud, Md. Rejaul Islam Royel, et al.
Genomics (2020) Vol. 112, Iss. 5, pp. 3416-3426
Open Access | Times Cited: 48
Efficient machine learning model for predicting drug-target interactions with case study for Covid-19
Heba El-Behery, Abdel-Fattah Attia, Nawal El‐Fishawy, et al.
Computational Biology and Chemistry (2021) Vol. 93, pp. 107536-107536
Open Access | Times Cited: 36
A geometric deep learning model for display and prediction of potential drug-virus interactions against SARS-CoV-2
Bihter Daş, Mucahit Kutsal, Resul Daş
Chemometrics and Intelligent Laboratory Systems (2022) Vol. 229, pp. 104640-104640
Open Access | Times Cited: 22
Improving prediction of drug-target interactions based on fusing multiple features with data balancing and feature selection techniques
Hakimeh Khojasteh, Jamshid Pirgazi, Ali Ghanbari Sorkhi
PLoS ONE (2023) Vol. 18, Iss. 8, pp. e0288173-e0288173
Open Access | Times Cited: 15
Drug–target interaction prediction based on protein features, using wrapper feature selection
Hengame Abbasi Mesrabadi, Karim Faez, Jamshid Pirgazi
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 13
An optimized automated drug-target interaction identification model based on a dual approach of 1D convolutional neural networks and self-attention-enhanced LSTMs
Tintu Vijayan, Pamela Vinitha Eric
International Journal of Information Technology (2025)
Closed Access
DeepACTION: A deep learning-based method for predicting novel drug-target interactions
Saifuddin Mahmud, Wenyu Chen, Hosney Jahan, et al.
Analytical Biochemistry (2020) Vol. 610, pp. 113978-113978
Closed Access | Times Cited: 34
Identification of common molecular signatures of SARS-CoV-2 infection and its influence on acute kidney injury and chronic kidney disease
Weiwei Zhang, Leping Liu, Xiangcheng Xiao, et al.
Frontiers in Immunology (2023) Vol. 14
Open Access | Times Cited: 12
PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features
Abel Chandra, Alok Sharma, Abdollah Dehzangi, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 12
Prediction of drug-target binding affinity based on deep learning models
Hao Zhang, Xiaoqian Liu, Wenya Cheng, et al.
Computers in Biology and Medicine (2024) Vol. 174, pp. 108435-108435
Closed Access | Times Cited: 4
Multi-view self-attention for interpretable drug–target interaction prediction
Brighter Agyemang, Weiping Wu, Michael Y. Kpiebaareh, et al.
Journal of Biomedical Informatics (2020) Vol. 110, pp. 103547-103547
Open Access | Times Cited: 29
DeepStack-DTIs: Predicting Drug–Target Interactions Using LightGBM Feature Selection and Deep-Stacked Ensemble Classifier
Yan Zhang, Zhiwen Jiang, Cheng Chen, et al.
Interdisciplinary Sciences Computational Life Sciences (2021) Vol. 14, Iss. 2, pp. 311-330
Closed Access | Times Cited: 25
Prediction of Linear Cationic Antimicrobial Peptides Active against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models
Ümmü Gülsüm Söylemez, Malik Yousef, Zülal Kesmen, et al.
Applied Sciences (2022) Vol. 12, Iss. 7, pp. 3631-3631
Open Access | Times Cited: 18
Vaccine development using artificial intelligence and machine learning: A review
Varun Asediya, Pranav Anjaria, R. A. Mathakiya, et al.
International Journal of Biological Macromolecules (2024), pp. 136643-136643
Closed Access | Times Cited: 3
Current status and future prospects of drug–target interaction prediction
Xiaoqing Ru, Xiucai Ye, Tetsuya Sakurai, et al.
Briefings in Functional Genomics (2021) Vol. 20, Iss. 5, pp. 312-322
Closed Access | Times Cited: 18
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