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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 antimicrobial minimal inhibitory concentrations for Neisseria gonorrhoeae using machine learning models
Muhammad Yasir, Asad Mustafa Karim, Sumera Kausar Malik, et al.
Saudi Journal of Biological Sciences (2022) Vol. 29, Iss. 5, pp. 3687-3693
Open Access | Times Cited: 27

Showing 1-25 of 27 citing articles:

Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation
Tabish Ali, Sarfaraz Ahmed, Muhammad Aslam
Antibiotics (2023) Vol. 12, Iss. 3, pp. 523-523
Open Access | Times Cited: 68
Prediction of crop yield in India using machine learning and hybrid deep learning models
Krithikha Sanju Saravanan, Velammal Bhagavathiappan
Acta Geophysica (2024) Vol. 72, Iss. 6, pp. 4613-4632
Closed Access | Times Cited: 7
Application of Decision-Tree-Based Machine Learning Algorithms for Prediction of Antimicrobial Resistance
Muhammad Yasir, Asad Mustafa Karim, Sumera Kausar Malik, et al.
Antibiotics (2022) Vol. 11, Iss. 11, pp. 1593-1593
Open Access | Times Cited: 22
Prediction of inhibitory peptides against E.coli with desired MIC value
Nisha Bajiya, Nishant Kumar, Gajendra P. S. Raghava
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Machine Learning-Driven Discovery of Highly Selective Antifungal Peptides Containing Non-Canonical β-Amino Acids
Douglas H. Chang, Joshua Richardson, Myung‐Ryul Lee, et al.
Chemical Science (2025)
Open Access
Schizophrenia and cell senescence candidate genes screening, machine learning, diagnostic models, and drug prediction
Yu Feng, Jing Shen, Jin He, et al.
Frontiers in Psychiatry (2023) Vol. 14
Open Access | Times Cited: 10
Bioinformatics analysis and prediction of Alzheimer’s disease and alcohol dependence based on Ferroptosis-related genes
Mei Tian, Jing Shen, Zhiqiang Qi, et al.
Frontiers in Aging Neuroscience (2023) Vol. 15
Open Access | Times Cited: 10
Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence
Doris Rušić, Marko Kumrić, Ana Šešelja Perišin, et al.
Microorganisms (2024) Vol. 12, Iss. 5, pp. 842-842
Open Access | Times Cited: 3
Prediction and interpretation of antibiotic-resistance genes occurrence at recreational beaches using machine learning models
Sara Iftikhar, Asad Mustafa Karim, Aoun Murtaza Karim, et al.
Journal of Environmental Management (2022) Vol. 328, pp. 116969-116969
Closed Access | Times Cited: 14
Predicting S. aureus antimicrobial resistance with interpretable genomic space maps
Karina Pikalyova, Alexey A. Orlov, Dragos Horvath, et al.
Molecular Informatics (2024) Vol. 43, Iss. 5
Open Access | Times Cited: 2
Quantitative drug susceptibility testing for Mycobacterium tuberculosis using unassembled sequencing data and machine learning

PLoS Computational Biology (2024) Vol. 20, Iss. 8, pp. e1012260-e1012260
Open Access | Times Cited: 2
An Ensemble Deep Learning Model for Predicting Minimum Inhibitory Concentrations of Antimicrobial Peptides Against Pathogenic Bacteria
Chia‐Ru Chung, Chung-Yu Chien, Yun Tang, et al.
iScience (2024) Vol. 27, Iss. 9, pp. 110718-110718
Open Access | Times Cited: 2
Antibiotic resistance genes prevalence prediction and interpretation in beaches affected by urban wastewater discharge
Qandeel Zahra, Jawaria Gul, Ali Raza Shah, et al.
One Health (2023) Vol. 17, pp. 100642-100642
Open Access | Times Cited: 6
Comparative genomics reveals the correlations of stress response genes and bacteriophages in developing antibiotic resistance of Staphylococcus saprophyticus
Kailun Zhang, Robert F. Potter, Jamie Marino, et al.
mSystems (2023) Vol. 8, Iss. 6
Open Access | Times Cited: 5
Machine learning analysis on the impacts of COVID-19 on India’s renewable energy transitions and air quality
Thompson Stephan, Fadi Al‐Turjman, Monica Ravishankar, et al.
Environmental Science and Pollution Research (2022) Vol. 29, Iss. 52, pp. 79443-79465
Open Access | Times Cited: 8
Prevalence of antibiotic resistance: an alarming threat for human
Bhargav Parmar, Gayatri Patel, Riteshkumar Arya
Vegetos (2024)
Closed Access | Times Cited: 1
Machine learning to predict ceftriaxone resistance using single nucleotide polymorphisms within a global database of Neisseria gonorrhoeae genomes
Sung Min Ha, Eric Y. Lin, Jeffrey D. Klausner, et al.
Microbiology Spectrum (2023) Vol. 11, Iss. 6
Open Access | Times Cited: 3
Predictive model, miRNA-TF network, related subgroup identification and drug prediction of ischemic stroke complicated with mental disorders based on genes related to gut microbiome
Jing Shen, Yu Feng, Minyan Lu, et al.
Frontiers in Neurology (2023) Vol. 14
Open Access | Times Cited: 2
Machine learning models for Neisseria gonorrhoeae antimicrobial susceptibility tests
Skylar L. Martin, Tatum D. Mortimer, Yonatan H. Grad
Annals of the New York Academy of Sciences (2022) Vol. 1520, Iss. 1, pp. 74-88
Open Access | Times Cited: 3
Genomic Epidemiology and Surveillance of Antimicrobial Resistance
Neris García‐González, Irving Cancino‐Muñoz, Leonor Sánchez-Busó, et al.
Elsevier eBooks (2024), pp. 291-316
Closed Access
Prediction of inhibitory peptides against E. coli with desired MIC value
Nisha Bajiya, Nishant Kumar, Gajendra P. S. Raghava
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Closed Access
Unveiling Neisseria gonorrhoeae Survival: Genetic Variability, Pathogenesis, and Antimicrobial Drug Resistance
Boris Shaskolskiy, Ilya Kandinov, Dmitry Gryadunov, et al.
Molecular Biology (2024) Vol. 58, Iss. 6, pp. 1003-1038
Closed Access
A novel coupling interpretable machine learning framework for water quality prediction and environmental effect understanding in different flow discharge regulations of hydro-projects
Xizhi Nong, Cheng Lai, Lihua Chen, et al.
The Science of The Total Environment (2024) Vol. 950, pp. 175281-175281
Closed Access
Quantitative drug susceptibility testing for M. tuberculosis using unassembled sequencing data and machine learning
Alexander S. Lachapelle
bioRxiv (Cold Spring Harbor Laboratory) (2021)
Open Access | Times Cited: 3
Predicting S. aureus antimicrobial resistance with interpretable genomic space maps
Karina Pikalyova, Alexey A. Orlov, Dragos Horvath, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access
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