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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:

AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest
Pratiti Bhadra, Jielu Yan, Jinyan Li, et al.
Scientific Reports (2018) Vol. 8, Iss. 1
Open Access | Times Cited: 249

Showing 1-25 of 249 citing articles:

The value of antimicrobial peptides in the age of resistance
Maria Magana, Pushpanathan Muthuirulan, Ana L. Santos, et al.
The Lancet Infectious Diseases (2020) Vol. 20, Iss. 9, pp. e216-e230
Closed Access | Times Cited: 842
Identification of antimicrobial peptides from the human gut microbiome using deep learning
Yue Ma, Zhengyan Guo, Binbin Xia, et al.
Nature Biotechnology (2022) Vol. 40, Iss. 6, pp. 921-931
Closed Access | Times Cited: 302
Deep-AmPEP30: Improve Short Antimicrobial Peptides Prediction with Deep Learning
Jielu Yan, Pratiti Bhadra, Ang Li, et al.
Molecular Therapy — Nucleic Acids (2020) Vol. 20, pp. 882-894
Open Access | Times Cited: 216
Large-Scale Analyses of Human Microbiomes Reveal Thousands of Small, Novel Genes
Hila Sberro, Brayon J. Fremin, Soumaya Zlitni, et al.
Cell (2019) Vol. 178, Iss. 5, pp. 1245-1259.e14
Open Access | Times Cited: 211
Computer-Aided Design of Antimicrobial Peptides: Are We Generating Effective Drug Candidates?
Marlon H. Cardoso, Raquel Q. Orozco, Samilla B. Rezende, et al.
Frontiers in Microbiology (2020) Vol. 10
Open Access | Times Cited: 182
mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides
Vinothini Boopathi, Sathiyamoorthy Subramaniyam, Adeel Malik, et al.
International Journal of Molecular Sciences (2019) Vol. 20, Iss. 8, pp. 1964-1964
Open Access | Times Cited: 167
sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure
Ke Yan, Hongwu Lv, Yichen Guo, et al.
Bioinformatics (2022) Vol. 39, Iss. 1
Open Access | Times Cited: 118
Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides
Jing Xu, Fuyi Li, André Leier, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 5
Closed Access | Times Cited: 112
Alternative therapeutic strategies to treat antibiotic-resistant pathogens
Craig R. MacNair, Steven T. Rutherford, Man‐Wah Tan
Nature Reviews Microbiology (2023) Vol. 22, Iss. 5, pp. 262-275
Closed Access | Times Cited: 81
Discovery of antimicrobial peptides in the global microbiome with machine learning
Célio Dias Santos Júnior, Marcelo D. T. Torres, Yiqian Duan, et al.
Cell (2024) Vol. 187, Iss. 14, pp. 3761-3778.e16
Open Access | Times Cited: 81
Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning
Jacqueline R. M. A. Maasch, Marcelo D. T. Torres, Marcelo C. R. Melo, et al.
Cell Host & Microbe (2023) Vol. 31, Iss. 8, pp. 1260-1274.e6
Open Access | Times Cited: 76
Biosynthesis, bioactivity, biotoxicity and applications of antimicrobial peptides for human health
Dai‐Xu Wei, Xu-Wei Zhang
Biosafety and Health (2022) Vol. 4, Iss. 2, pp. 118-134
Open Access | Times Cited: 68
Antimicrobial peptides: An alternative to traditional antibiotics
Shuaiqi Ji, Feiyu An, Taowei Zhang, et al.
European Journal of Medicinal Chemistry (2023) Vol. 265, pp. 116072-116072
Closed Access | Times Cited: 68
Machine learning for antimicrobial peptide identification and design
Fangping Wan, Felix Wong, James J. Collins, et al.
Nature Reviews Bioengineering (2024) Vol. 2, Iss. 5, pp. 392-407
Closed Access | Times Cited: 42
Mining human microbiomes reveals an untapped source of peptide antibiotics
Marcelo D. T. Torres, Erin F. Brooks, Angela Cesaro, et al.
Cell (2024) Vol. 187, Iss. 19, pp. 5453-5467.e15
Open Access | Times Cited: 37
Explainable deep learning and virtual evolution identifies antimicrobial peptides with activity against multidrug-resistant human pathogens
Beilun Wang, Peijun Lin, Yi Zhong, et al.
Nature Microbiology (2025)
Closed Access | Times Cited: 7
Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance
Angela Cesaro, Samuel C. Hoffman, Payel Das, et al.
npj Antimicrobials and Resistance (2025) Vol. 3, Iss. 1
Open Access | Times Cited: 6
AI Methods for Antimicrobial Peptides: Progress and Challenges
Carlos A. Brizuela, Gary Liu, J Stokes, et al.
Microbial Biotechnology (2025) Vol. 18, Iss. 1
Open Access | Times Cited: 2
iBCE-EL: A New Ensemble Learning Framework for Improved Linear B-Cell Epitope Prediction
Balachandran Manavalan, Rajiv Gandhi Govindaraj, Tae Hwan Shin, et al.
Frontiers in Immunology (2018) Vol. 9
Open Access | Times Cited: 159
dbAMP: an integrated resource for exploring antimicrobial peptides with functional activities and physicochemical properties on transcriptome and proteome data
Jhih-Hua Jhong, Yu-Hsiang Chi, Wen-Chi Li, et al.
Nucleic Acids Research (2018) Vol. 47, Iss. D1, pp. D285-D297
Open Access | Times Cited: 134
Characterization and identification of antimicrobial peptides with different functional activities
Chia‐Ru Chung, Ting-Rung Kuo, Li-Ching Wu, et al.
Briefings in Bioinformatics (2019) Vol. 21, Iss. 3, pp. 1098-1114
Closed Access | Times Cited: 132
PreAIP: Computational Prediction of Anti-inflammatory Peptides by Integrating Multiple Complementary Features
Mst. Shamima Khatun, Md Mehedi Hasan, Hiroyuki Kurata
Frontiers in Genetics (2019) Vol. 10
Open Access | Times Cited: 131
amPEPpy 1.0: a portable and accurate antimicrobial peptide prediction tool
Travis J. Lawrence, Dana L. Carper, Margaret K. Spangler, et al.
Bioinformatics (2020) Vol. 37, Iss. 14, pp. 2058-2060
Open Access | Times Cited: 83
Encodings and models for antimicrobial peptide classification for multi-resistant pathogens
Sebastian Spänig, Dominik Heider
BioData Mining (2019) Vol. 12, Iss. 1
Open Access | Times Cited: 82
xDeep-AcPEP: Deep Learning Method for Anticancer Peptide Activity Prediction Based on Convolutional Neural Network and Multitask Learning
Jiarui Chen, Hong Hin Cheong, Shirley W. I. Siu
Journal of Chemical Information and Modeling (2021) Vol. 61, Iss. 8, pp. 3789-3803
Open Access | Times Cited: 79
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