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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:

ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides
Leyi Wei, Chen Zhou, Huangrong Chen, et al.
Bioinformatics (2018) Vol. 34, Iss. 23, pp. 4007-4016
Open Access | Times Cited: 386

Showing 1-25 of 386 citing articles:

Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening
Shaherin Basith, Balachandran Manavalan, Tae Hwan Shin, et al.
Medicinal Research Reviews (2020) Vol. 40, Iss. 4, pp. 1276-1314
Closed Access | Times Cited: 253
Deep-Resp-Forest: A deep forest model to predict anti-cancer drug response
Ran Su, Xinyi Liu, Leyi Wei, et al.
Methods (2019) Vol. 166, pp. 91-102
Closed Access | Times Cited: 225
mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, et al.
Bioinformatics (2018) Vol. 35, Iss. 16, pp. 2757-2765
Closed Access | Times Cited: 221
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
Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, et al.
Molecular Therapy — Nucleic Acids (2019) Vol. 16, pp. 733-744
Open Access | Times Cited: 199
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: 192
HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation
Md Mehedi Hasan, Nalini Schaduangrat, Shaherin Basith, et al.
Bioinformatics (2020) Vol. 36, Iss. 11, pp. 3350-3356
Closed Access | Times Cited: 181
Identify origin of replication inSaccharomyces cerevisiaeusing two-step feature selection technique
Fanny Dao, Hao Lv, Fang Wang, et al.
Bioinformatics (2018) Vol. 35, Iss. 12, pp. 2075-2083
Closed Access | Times Cited: 180
ACP-DL: A Deep Learning Long Short-Term Memory Model to Predict Anticancer Peptides Using High-Efficiency Feature Representation
Hai-Cheng Yi, Zhu‐Hong You, Xi Zhou, et al.
Molecular Therapy — Nucleic Acids (2019) Vol. 17, pp. 1-9
Open Access | Times Cited: 177
Application of Machine Learning in Microbiology
Kaiyang Qu, Fei Guo, Xiangrong Liu, et al.
Frontiers in Microbiology (2019) Vol. 10
Open Access | Times Cited: 169
Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species
Leyi Wei, Shasha Luan, Luís Augusto Eijy Nagai, et al.
Bioinformatics (2018) Vol. 35, Iss. 8, pp. 1326-1333
Closed Access | Times Cited: 168
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
PEPred-Suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning
Leyi Wei, Chen Zhou, Ran Su, et al.
Bioinformatics (2019) Vol. 35, Iss. 21, pp. 4272-4280
Closed Access | Times Cited: 161
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: 117
Anticancer peptides prediction with deep representation learning features
Zhibin Lv, Feifei Cui, Quan Zou, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 5
Closed Access | Times Cited: 113
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: 111
StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides
Phasit Charoenkwan, Wararat Chiangjong, Chanin Nantasenamat, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Closed Access | Times Cited: 109
sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks
Mengting Niu, Yuan Lin, Quan Zou
Plant Molecular Biology (2021) Vol. 105, Iss. 4-5, pp. 483-495
Closed Access | Times Cited: 103
Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding
Qitong Yuan, Keyi Chen, Yimin Yu, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 1
Closed Access | Times Cited: 72
Biological Sequence Classification: A Review on Data and General Methods
Chunyan Ao, Shihu Jiao, Yansu Wang, et al.
Research (2022) Vol. 2022
Open Access | Times Cited: 68
Accurately identifying hemagglutinin using sequence information and machine learning methods
Xidan Zou, Liping Ren, Peiling Cai, et al.
Frontiers in Medicine (2023) Vol. 10
Open Access | Times Cited: 67
Anti-Cancer Peptides: Status and Future Prospects
Gehane Ghaly, Hatem Tallima, Eslam Dabbish, et al.
Molecules (2023) Vol. 28, Iss. 3, pp. 1148-1148
Open Access | Times Cited: 40
Bitter-RF: A random forest machine model for recognizing bitter peptides
Yufei Zhang, Yuhao Wang, Zhi-Feng Gu, et al.
Frontiers in Medicine (2023) Vol. 10
Open Access | Times Cited: 40
iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities
Jing Xu, Fuyi Li, Chen Li, et al.
Briefings in Bioinformatics (2023) Vol. 24, Iss. 4
Open Access | Times Cited: 40
SSCRB: Predicting circRNA-RBP Interaction Sites Using a Sequence and Structural Feature-Based Attention Model
Liwei Liu, Yuxiao Wei, Qi Zhang, et al.
IEEE Journal of Biomedical and Health Informatics (2024) Vol. 28, Iss. 3, pp. 1762-1772
Closed Access | Times Cited: 17
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