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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:

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

Showing 9 citing articles:

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
Deep Learning Techniques Applied to Phosphorylation Site Prediction: A Systematic Review
Antônio Carlos da Silva Júnior, André Massahiro Shimaoka, Luciano Rodrigo Lopes, et al.
SN Computer Science (2025) Vol. 6, Iss. 4
Closed Access
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
Accurately predicting microbial phosphorylation sites using evolutionary and structural features
Faisal Ahmed, Abdollah Dehzangi, Md Mehedi Hasan, et al.
Gene (2022) Vol. 851, pp. 146993-146993
Closed Access | Times Cited: 5
An Improved Computational Prediction Model for Lysine Succinylation Sites Mapping on Homo sapiens by Fusing Three Sequence Encoding Schemes with the Random Forest Classifier
Samme Amena Tasmia, Fee Faysal Ahmed, Parvez Mosharaf, et al.
Current Genomics (2021) Vol. 22, Iss. 2, pp. 122-136
Open Access | Times Cited: 6
SubFeat: Feature subspacing ensemble classifier for function prediction of DNA, RNA and protein sequences
H. M. Fazlul Haque, Rafsanjani Muhammod, Fariha Arifin, et al.
Computational Biology and Chemistry (2021) Vol. 92, pp. 107489-107489
Open Access | Times Cited: 6
Improved Prediction of Protein-Protein Interaction Mapping on Homo Sapiens by Using Amino Acid Sequence Features in a Supervised Learning Framework
Md. Merajul Islam, Md. Jahangir Alam, Fee Faysal Ahmed, et al.
Protein and Peptide Letters (2020) Vol. 28, Iss. 1, pp. 74-83
Closed Access | Times Cited: 5
Identification of phosphorylation site using S-padding strategy based convolutional neural network
Yanjiao Zeng, Dongning Liu, Yang Wang
Health Information Science and Systems (2022) Vol. 10, Iss. 1
Open Access | Times Cited: 3
DeepPhoPred: Accurate Deep Learning Model to Predict Microbial Phosphorylation
Faisal Ahmed, Alok Sharma, Swakkhar Shatabda, et al.
Proteins Structure Function and Bioinformatics (2024)
Closed Access
SubFeat: Feature Subspacing Ensemble Classifier for Function Prediction of DNA, RNA and Protein Sequences
H. M. Fazlul Haque, Fariha Arifin, Sheikh Adilina, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2020)
Open Access | Times Cited: 1
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