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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:

A deep learning method to more accurately recall known lysine acetylation sites
Meiqi Wu, Yingxi Yang, Hui Wang, et al.
BMC Bioinformatics (2019) Vol. 20, Iss. 1
Open Access | Times Cited: 49

Showing 26-50 of 49 citing articles:

MDCAN-Lys: A Model for Predicting Succinylation Sites Based on Multilane Dense Convolutional Attention Network
Huiqing Wang, Hong Sheng Zhao, Zhiliang Yan, et al.
Biomolecules (2021) Vol. 11, Iss. 6, pp. 872-872
Open Access | Times Cited: 16
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
Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks
Yingxi Yang, Hui Wang, Wen Li, et al.
BMC Bioinformatics (2021) Vol. 22, Iss. 1
Open Access | Times Cited: 14
predML-Site: Predicting Multiple Lysine PTM Sites With Optimal Feature Representation and Data Imbalance Minimization
Sabit Ahmed, Afrida Rahman, Md. Al Mehedi Hasan, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2021) Vol. 19, Iss. 6, pp. 3624-3634
Closed Access | Times Cited: 14
A protein succinylation sites prediction method based on the hybrid architecture of LSTM network and CNN
Die Zhang, Shunfang Wang
Journal of Bioinformatics and Computational Biology (2022) Vol. 20, Iss. 02
Closed Access | Times Cited: 9
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
Computational identification of multiple lysine PTM sites by analyzing the instance hardness and feature importance
Sabit Ahmed, Afrida Rahman, Md. Al Mehedi Hasan, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 12
ECM-LSE: Prediction of Extracellular Matrix Proteins Using Deep Latent Space Encoding of k-Spaced Amino Acid Pairs
Ubaid M. Al‐Saggaf, Muhammad Usman, Imran Naseem, et al.
Frontiers in Bioengineering and Biotechnology (2021) Vol. 9
Open Access | Times Cited: 12
Sitetack: A deep learning model that improves PTM prediction by using known PTMs
Clair S. Gutierrez, Alia A. Kassim, Benjamin D. Gutierrez, et al.
Bioinformatics (2024) Vol. 40, Iss. 11
Open Access | Times Cited: 1
O-GlyThr: Prediction of human O-linked threonine glycosites using multi-feature fusion
Hua Tang, Qiang Tang, Qian Zhang, et al.
International Journal of Biological Macromolecules (2023) Vol. 242, pp. 124761-124761
Closed Access | Times Cited: 4
A deep neural network-based approach for seizure activity recognition of epilepsy sufferers
Danial Khurshid, Fazli Wahid, Sikandar Ali, et al.
Frontiers in Medicine (2024) Vol. 11
Open Access | Times Cited: 1
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
Artificial Intelligence Transforming Post-Translational Modification Research
Doo Nam Kim, Tianzhixi Yin, Tong Zhang, et al.
Bioengineering (2024) Vol. 12, Iss. 1, pp. 26-26
Open Access | Times Cited: 1
predPhogly-Site: Predicting phosphoglycerylation sites by incorporating probabilistic sequence-coupling information into PseAAC and addressing data imbalance
Sabit Ahmed, Afrida Rahman, Md. Al Mehedi Hasan, et al.
PLoS ONE (2021) Vol. 16, Iss. 4, pp. e0249396-e0249396
Open Access | Times Cited: 10
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
System-Wide Analysis of Protein Acetylation and Ubiquitination Reveals a Diversified Regulation in Human Cancer Cells
Hiroko Kozuka‐Hata, Aya Kitamura, Tomoko Hiroki, et al.
Biomolecules (2020) Vol. 10, Iss. 3, pp. 411-411
Open Access | Times Cited: 9
Interpretable machine learning identification of arginine methylation sites
Syed Danish Ali, Hilal Tayara, Kil To Chong
Computers in Biology and Medicine (2022) Vol. 147, pp. 105767-105767
Closed Access | Times Cited: 6
LipoSVM: Prediction of Lysine lipoylation in Proteins based on the Support Vector Machine
Meiqi Wu, Pengchao Lu, Yingxi Yang, et al.
Current Genomics (2019) Vol. 20, Iss. 5, pp. 362-370
Open Access | Times Cited: 6
TransPTM: a Transformer-Based Model for Non-Histone Acetylation Site Prediction
Lingkuan Meng, Xingjian Chen, Ke Cheng, et al.
(2023)
Open Access | Times Cited: 2
UbNiRF: A Hybrid Framework Based on Null Importances and Random Forest that Combines Multiple Features to Predict Ubiquitination Sites in Arabidopsis thaliana and Homo sapiens
Xiaokang Li, Zheming Yuan, Yuan Chen
Frontiers in Bioscience-Landmark (2024) Vol. 29, Iss. 5
Open Access
Prediction Method for Lysine Acetylation Sites Based on LSTM Network
Qingxiao Xiu, Dancheng Li, Hailong Li, et al.
(2019), pp. 179-182
Closed Access | Times Cited: 2
predForm-Site: Formylation site prediction by incorporating multiple features and resolving data imbalance
Md Khaled Ben Islam, Julia Rahman, Md. Al Mehedi Hasan, et al.
Computational Biology and Chemistry (2021) Vol. 94, pp. 107553-107553
Closed Access | Times Cited: 2
Predicting cysteine reactivity changes upon phosphorylation using XGBoost
Jing Cao, Yan Xu
FEBS Open Bio (2023) Vol. 14, Iss. 1, pp. 51-62
Open Access
Prediction of lysine HMGylation sites using multiple feature extraction and fuzzy support vector machine
Zhe Ju, Shiyun Wang
Analytical Biochemistry (2022) Vol. 663, pp. 115032-115032
Closed Access
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