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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:

Computational identification of microbial phosphorylation sites by the enhanced characteristics of sequence information
Md Mehedi Hasan, Md Mamunur Rashid, Mst. Shamima Khatun, et al.
Scientific Reports (2019) Vol. 9, Iss. 1
Open Access | Times Cited: 38

Showing 1-25 of 38 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: 254
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: 94
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
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
iTTCA-Hybrid: Improved and robust identification of tumor T cell antigens by utilizing hybrid feature representation
Phasit Charoenkwan, Chanin Nantasenamat, Md Mehedi Hasan, et al.
Analytical Biochemistry (2020) Vol. 599, pp. 113747-113747
Closed Access | Times Cited: 52
A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction
Farzaneh Esmaili, Mahdi Pourmirzaei, Shahin Ramazi, et al.
Genomics Proteomics & Bioinformatics (2023) Vol. 21, Iss. 6, pp. 1266-1285
Open Access | Times Cited: 17
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
iHyd-LysSite (EPSV): Identifying Hydroxylysine Sites in Protein Using Statistical Formulation by Extracting Enhanced Position and Sequence Variant Feature Technique
Muhammad Khalid Mahmood, Asma Ehsan, Yaser Daanial Khan, et al.
Current Genomics (2020) Vol. 21, Iss. 7, pp. 536-545
Open Access | Times Cited: 44
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
iAMY-SCM: Improved prediction and analysis of amyloid proteins using a scoring card method with propensity scores of dipeptides
Phasit Charoenkwan, Sakawrat Kanthawong, Chanin Nantasenamat, et al.
Genomics (2020) Vol. 113, Iss. 1, pp. 689-698
Open Access | Times Cited: 38
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
Importance of protein Ser/Thr/Tyr phosphorylation for bacterial pathogenesis
Julie Bonne Køhler, Carsten Jers, Mériem Senissar, et al.
FEBS Letters (2020) Vol. 594, Iss. 15, pp. 2339-2369
Open Access | Times Cited: 33
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
dbPSP 2.0, an updated database of protein phosphorylation sites in prokaryotes
Ying Shi, Ying Zhang, Shaofeng Lin, et al.
Scientific Data (2020) Vol. 7, Iss. 1
Open Access | Times Cited: 29
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
Predicting protein phosphorylation sites in soybean using interpretable deep tabular learning network
Elham Khalili, Shahin Ramazi, Faezeh Ghanati, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 2
Closed Access | Times Cited: 18
iPhoPred: A Predictor for Identifying Phosphorylation Sites in Human Protein
Shi-Hao Li, Jun Zhang, Ya-Wei Zhao, et al.
IEEE Access (2019) Vol. 7, pp. 177517-177528
Open Access | Times Cited: 25
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
Experimental measurement and computational prediction of bacterial Hanks‐type Ser/Thr signaling system regulatory targets
Noam Grunfeld, Erel Levine, Elizabeth A. Libby
Molecular Microbiology (2024) Vol. 122, Iss. 2, pp. 152-164
Open Access | Times Cited: 2
MDC-Kace: A Model for Predicting Lysine Acetylation Sites Based on Modular Densely Connected Convolutional Networks
Huiqing Wang, Zhiliang Yan, Dan Liu, et al.
IEEE Access (2020) Vol. 8, pp. 214469-214480
Open Access | Times Cited: 14
Recent Development of Machine Learning Methods in Microbial Phosphorylation Sites
Md Mamunur Rashid, Swakkhar Shatabda, Md Mehedi Hasan, et al.
Current Genomics (2020) Vol. 21, Iss. 3, pp. 194-203
Open Access | Times Cited: 9
Review and Comparative Analysis of Machine Learning-based Predictors for Predicting and Analyzing Anti-angiogenic Peptides
Phasit Charoenkwan, Wararat Chiangjong, Md Mehedi Hasan, et al.
Current Medicinal Chemistry (2021) Vol. 29, Iss. 5, pp. 849-864
Closed Access | Times Cited: 9
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