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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:

RF-MaloSite and DL-Malosite: Methods based on random forest and deep learning to identify malonylation sites
Hussam J. AL-barakati, Niraj Thapa, Hiroto Saigo, et al.
Computational and Structural Biotechnology Journal (2020) Vol. 18, pp. 852-860
Open Access | Times Cited: 21

Showing 21 citing articles:

A survey on deep learning in medicine: Why, how and when?
Francesco Piccialli, Vittorio Di Somma, Fabio Giampaolo, et al.
Information Fusion (2020) Vol. 66, pp. 111-137
Closed Access | Times Cited: 288
StackACPred: Prediction of anticancer peptides by integrating optimized multiple feature descriptors with stacked ensemble approach
Muhammad Arif, Saeed Ahmed, Fang Ge, et al.
Chemometrics and Intelligent Laboratory Systems (2021) Vol. 220, pp. 104458-104458
Closed Access | Times Cited: 53
Post-translational modification prediction via prompt-based fine-tuning of a GPT-2 model
Palistha Shrestha, Jeevan Kandel, Hilal Tayara, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 6
Malsite-Deep: Prediction of protein malonylation sites through deep learning and multi-information fusion based on NearMiss-2 strategy
Minghui Wang, Lili Song, Yaqun Zhang, et al.
Knowledge-Based Systems (2022) Vol. 240, pp. 108191-108191
Closed Access | Times Cited: 22
Analysis and review of techniques and tools based on machine learning and deep learning for prediction of lysine malonylation sites in protein sequences
Shahin Ramazi, Seyed Amir Hossein Tabatabaei, Elham Khalili, et al.
Database (2024) Vol. 2024
Open Access | Times Cited: 5
Systematic qualitative proteome-wide analysis of lysine malonylation profiling in Platycodon grandiflorus
Qingshan Yang, Shaowei Xu, Weimin Jiang, et al.
Amino Acids (2025) Vol. 57, Iss. 1
Open Access
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
Advances, obstacles, and opportunities for machine learning in proteomics
Heather Desaire, Eden P. Go, David Hua
Cell Reports Physical Science (2022) Vol. 3, Iss. 10, pp. 101069-101069
Open Access | Times Cited: 17
Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features
Md. Easin Arafat, Md. Wakil Ahmad, S.M. Shovan, et al.
Genes (2020) Vol. 11, Iss. 9, pp. 1023-1023
Open Access | Times Cited: 24
Deep Learning–Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction
Subash C. Pakhrin, Suresh Pokharel, Hiroto Saigo, et al.
Methods in molecular biology (2022), pp. 285-322
Closed Access | Times Cited: 15
DTL-DephosSite: Deep Transfer Learning Based Approach to Predict Dephosphorylation Sites
Meenal Chaudhari, Niraj Thapa, Hamid D. Ismail, et al.
Frontiers in Cell and Developmental Biology (2021) Vol. 9
Open Access | Times Cited: 15
A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites
Niraj Thapa, Meenal Chaudhari, Anthony A. Iannetta, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 14
Current computational tools for protein lysine acylation site prediction
Zhaohui Qin, Haoran Ren, Pei Zhao, et al.
Briefings in Bioinformatics (2024) Vol. 25, Iss. 6
Closed Access | Times Cited: 1
A hybrid feature extraction scheme for efficient malonylation site prediction
Ali Ghanbari Sorkhi, Jamshid Pirgazi, Vahid Ghasemi
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 7
Post-Translational Modification Prediction via Prompt-Based Fine-Tuning of a GPT-2 Model
Palistha Shrestha, Jeevan Kandel, Hilal Tayara, et al.
Research Square (Research Square) (2024)
Open Access
Network Intrusion Detection System using Federated Machine Learning Approach
R. Padmashani, R. Harshan, C. Logeshwaran, et al.
International Journal of Innovative Science and Research Technology (IJISRT) (2024), pp. 212-219
Open Access
Extensive comparison of protein sequence-based bioinformatics applications for predicting lysine succinylation sites: a comparative review
Hussam Alsharif
Biotechnology & Biotechnological Equipment (2024) Vol. 38, Iss. 1
Open Access
Bioinformatic Analyses of Peroxiredoxins and RF-Prx: A Random Forest-Based Predictor and Classifier for Prxs
Hussam J. AL-barakati, Robert H. Newman, Dukka B. KC, et al.
Methods in molecular biology (2022), pp. 155-176
Open Access | Times Cited: 2
Chlamy-EnPhosSite: A deep learning-based approach for Chlamydomonas reinhardtii-specific phosphorylation site prediction
Niraj Thapa, Meenal Chaudhari, Anthony A. Iannetta, et al.
Research Square (Research Square) (2021)
Closed Access | Times Cited: 2
LSTM_Kmal: Prediction of Malonylation Based on LSTM and Feature Confusion
Xin Liu, Xia-Wei Dai, Zhi-Ao Xu, et al.
(2023), pp. 158-164
Closed Access
Systematic Qualitative Proteome-wide Analysis of Lysine Malonylation Profiling in Platycodon grandiflorus
Qingshan Yang, Shaowei Xu, Weimin Jiang, et al.
Research Square (Research Square) (2022)
Open Access
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