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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:

Efficient computational model for identification of antitubercular peptides by integrating amino acid patterns and properties
Shamima Khatun, Md Mehedi Hasan, Hiroyuki Kurata
FEBS Letters (2019) Vol. 593, Iss. 21, pp. 3029-3039
Open Access | Times Cited: 38

Showing 1-25 of 38 citing articles:

DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics
Malak Pirtskhalava, Anthony A Amstrong, Maia Grigolava, et al.
Nucleic Acids Research (2020) Vol. 49, Iss. D1, pp. D288-D297
Open Access | Times Cited: 419
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: 254
Antimicrobial Peptides: An Update on Classifications and Databases
Ahmer Bin Hafeez, Xukai Jiang, Phillip J. Bergen, et al.
International Journal of Molecular Sciences (2021) Vol. 22, Iss. 21, pp. 11691-11691
Open Access | Times Cited: 211
pAtbP-EnC: Identifying Anti-Tubercular Peptides Using Multi-Feature Representation and Genetic Algorithm-Based Deep Ensemble Model
Shahid Akbar, Ali Raza, Tamara Al Shloul, et al.
IEEE Access (2023) Vol. 11, pp. 137099-137114
Open Access | Times Cited: 53
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
i4mC-ROSE, a bioinformatics tool for the identification of DNA N4-methylcytosine sites in the Rosaceae genome
Md Mehedi Hasan, Balachandran Manavalan, Mst. Shamima Khatun, et al.
International Journal of Biological Macromolecules (2019) Vol. 157, pp. 752-758
Closed Access | Times Cited: 82
i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation
Md Mehedi Hasan, Balachandran Manavalan, Watshara Shoombuatong, et al.
Plant Molecular Biology (2020) Vol. 103, Iss. 1-2, pp. 225-234
Closed Access | Times Cited: 77
iAtbP-Hyb-EnC: Prediction of antitubercular peptides via heterogeneous feature representation and genetic algorithm based ensemble learning model
Shahid Akbar, Ashfaq Ahmad, Maqsood Hayat, et al.
Computers in Biology and Medicine (2021) Vol. 137, pp. 104778-104778
Closed Access | Times Cited: 76
Research progress in the screening and evaluation of umami peptides
Lulu Qi, Xinchang Gao, Daodong Pan, et al.
Comprehensive Reviews in Food Science and Food Safety (2022) Vol. 21, Iss. 2, pp. 1462-1490
Closed Access | Times Cited: 59
i4mC-Mouse: Improved identification of DNA N4-methylcytosine sites in the mouse genome using multiple encoding schemes
Md Mehedi Hasan, Balachandran Manavalan, Watshara Shoombuatong, et al.
Computational and Structural Biotechnology Journal (2020) Vol. 18, pp. 906-912
Open Access | Times Cited: 66
Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases
David A. Winkler
Frontiers in Chemistry (2021) Vol. 9
Open Access | Times Cited: 51
Bacteria-Specific Feature Selection for Enhanced Antimicrobial Peptide Activity Predictions Using Machine-Learning Methods
Hamid Teimouri, Angela Medvedeva, Anatoly B. Kolomeisky
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 6, pp. 1723-1733
Closed Access | Times Cited: 20
Prediction of S-nitrosylation sites by integrating support vector machines and random forest
Md Mehedi Hasan, Balachandran Manavalan, Mst. Shamima Khatun, et al.
Molecular Omics (2019) Vol. 15, Iss. 6, pp. 451-458
Closed Access | Times Cited: 51
MPMABP: A CNN and Bi-LSTM-Based Method for Predicting Multi-Activities of Bioactive Peptides
You Li, Xueyong Li, Yuewu Liu, et al.
Pharmaceuticals (2022) Vol. 15, Iss. 6, pp. 707-707
Open Access | Times Cited: 23
Critical evaluation of web-based DNA N6-methyladenine site prediction tools
Md Mehedi Hasan, Watshara Shoombuatong, Hiroyuki Kurata, et al.
Briefings in Functional Genomics (2020) Vol. 20, Iss. 4, pp. 258-272
Closed Access | Times Cited: 37
ProIn-Fuse: improved and robust prediction of proinflammatory peptides by fusing of multiple feature representations
Mst. Shamima Khatun, Md Mehedi Hasan, Watshara Shoombuatong, et al.
Journal of Computer-Aided Molecular Design (2020) Vol. 34, Iss. 12, pp. 1229-1236
Closed Access | Times Cited: 36
Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction
Mst. Shamima Khatun, Watshara Shoombuatong, Md Mehedi Hasan, et al.
Current Genomics (2020) Vol. 21, Iss. 6, pp. 454-463
Open Access | Times Cited: 32
In Silico Approaches for the Prediction and Analysis of Antiviral Peptides: A Review
Phasit Charoenkwan, Nuttapat Anuwongcharoen, Chanin Nantasenamat, et al.
Current Pharmaceutical Design (2020) Vol. 27, Iss. 18, pp. 2180-2188
Closed Access | Times Cited: 30
Computational prediction of protein ubiquitination sites mapping on Arabidopsis thaliana
Md. Parvez Mosharaf, Md. Mehedi Hassan, Fee Faysal Ahmed, et al.
Computational Biology and Chemistry (2020) Vol. 85, pp. 107238-107238
Closed Access | Times Cited: 28
Stack-DHUpred: Advancing the accuracy of dihydrouridine modification sites detection via stacking approach
Md. Harun-Or-Roshid, Kazuhiro Maeda, Le Thi Phan, et al.
Computers in Biology and Medicine (2023) Vol. 169, pp. 107848-107848
Closed Access | Times Cited: 10
PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features
Andi Nur Nilamyani, Firda Nurul Auliah, Mohammad Ali Moni, et al.
International Journal of Molecular Sciences (2021) Vol. 22, Iss. 5, pp. 2704-2704
Open Access | Times Cited: 18
Recent Development of Bioinformatics Tools for microRNA Target Prediction
Mst. Shamima Khatun, Md. Ashad Alam, Watshara Shoombuatong, et al.
Current Medicinal Chemistry (2021) Vol. 29, Iss. 5, pp. 865-880
Closed Access | Times Cited: 18
Bioinformatics and bioactive peptides from foods: Do they work together?
Anna Iwaniak, Piotr Mińkiewicz, Małgorzata Darewicz
Advances in food and nutrition research (2024), pp. 35-111
Closed Access | Times Cited: 2
Meta-2OM: A multi-classifier meta-model for the accurate prediction of RNA 2′-O-methylation sites in human RNA
Md. Harun-Or-Roshid, Nhat Truong Pham, Balachandran Manavalan, et al.
PLoS ONE (2024) Vol. 19, Iss. 6, pp. e0305406-e0305406
Open Access | Times Cited: 2
Interpretable molecular encodings and representations for machine learning tasks
Moritz Weckbecker, Aleksandar Anžel, Zewen Yang, et al.
Computational and Structural Biotechnology Journal (2024) Vol. 23, pp. 2326-2336
Open Access | Times Cited: 1
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