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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:

CPPred-FL: a sequence-based predictor for large-scale identification of cell-penetrating peptides by feature representation learning
Xiaoli Qiang, Chen Zhou, Xiucai Ye, et al.
Briefings in Bioinformatics (2018)
Closed Access | Times Cited: 121

Showing 1-25 of 121 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: 256
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: 225
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: 200
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: 184
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: 168
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: 164
ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides
B. Dharma Rao, Chen Zhou, Guoying Zhang, et al.
Briefings in Bioinformatics (2019) Vol. 21, Iss. 5, pp. 1846-1855
Closed Access | Times Cited: 147
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: 111
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: 73
Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools
Ran Su, Jie Hu, Quan Zou, et al.
Briefings in Bioinformatics (2018) Vol. 21, Iss. 2, pp. 408-420
Closed Access | Times Cited: 148
StackCPPred: a stacking and pairwise energy content-based prediction of cell-penetrating peptides and their uptake efficiency
Xiangzheng Fu, Lijun Cai, Xiangxiang Zeng, et al.
Bioinformatics (2020) Vol. 36, Iss. 10, pp. 3028-3034
Open Access | Times Cited: 124
Meta-i6mA: an interspecies predictor for identifying DNAN6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework
Md Mehedi Hasan, Shaherin Basith, Mst. Shamima Khatun, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 3
Closed Access | Times Cited: 114
Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework
Leyi Wei, Wenjia He, Adeel Malik, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 4
Closed Access | Times Cited: 114
iGHBP: Computational identification of growth hormone binding proteins from sequences using extremely randomised tree
Shaherin Basith, Balachandran Manavalan, Tae Hwan Shin, et al.
Computational and Structural Biotechnology Journal (2018) Vol. 16, pp. 412-420
Open Access | Times Cited: 111
AtbPpred: A Robust Sequence-Based Prediction of Anti-Tubercular Peptides Using Extremely Randomized Trees
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, et al.
Computational and Structural Biotechnology Journal (2019) Vol. 17, pp. 972-981
Open Access | Times Cited: 95
4mCpred-EL: An Ensemble Learning Framework for Identification of DNA N4-Methylcytosine Sites in the Mouse Genome
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, et al.
Cells (2019) Vol. 8, Iss. 11, pp. 1332-1332
Open Access | Times Cited: 91
Predicting cell-penetrating peptides using machine learning algorithms and navigating in their chemical space
Ewerton Cristhian Lima de Oliveira, Kauê Santana da Costa, Luiz Patrick Cordeiro Josino, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 87
NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning
Md Mehedi Hasan, Md. Ashad Alam, Watshara Shoombuatong, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Closed Access | Times Cited: 78
UMPred-FRL: A New Approach for Accurate Prediction of Umami Peptides Using Feature Representation Learning
Phasit Charoenkwan, Chanin Nantasenamat, Md Mehedi Hasan, et al.
International Journal of Molecular Sciences (2021) Vol. 22, Iss. 23, pp. 13124-13124
Open Access | Times Cited: 72
Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method
Phasit Charoenkwan, Wararat Chiangjong, Vannajan Sanghiran Lee, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 67
Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products
Kauê Santana da Costa, Lidiane Diniz do Nascimento, Anderson Lima e Lima, et al.
Frontiers in Chemistry (2021) Vol. 9
Open Access | Times Cited: 66
Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy
Md Mehedi Hasan, Sho Tsukiyama, Jae Youl Cho, et al.
Molecular Therapy (2022) Vol. 30, Iss. 8, pp. 2856-2867
Open Access | Times Cited: 66
StackDPPIV: A novel computational approach for accurate prediction of dipeptidyl peptidase IV (DPP-IV) inhibitory peptides
Phasit Charoenkwan, Chanin Nantasenamat, Md Mehedi Hasan, et al.
Methods (2021) Vol. 204, pp. 189-198
Closed Access | Times Cited: 60
TPpred-ATMV: therapeutic peptide prediction by adaptive multi-view tensor learning model
Ke Yan, Hongwu Lv, Yichen Guo, et al.
Bioinformatics (2022) Vol. 38, Iss. 10, pp. 2712-2718
Closed Access | Times Cited: 49
SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins
Phasit Charoenkwan, Nalini Schaduangrat, Mohammad Ali Moni, et al.
Computers in Biology and Medicine (2022) Vol. 146, pp. 105704-105704
Closed Access | Times Cited: 47
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