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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:

DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction
Niraj Thapa, Meenal Chaudhari, Sean McManus, et al.
BMC Bioinformatics (2020) Vol. 21, Iss. S3
Open Access | Times Cited: 57

Showing 1-25 of 57 citing articles:

An extended machine learning technique for polycystic ovary syndrome detection using ovary ultrasound image
Sayma Alam Suha, Muhammad Nazrul Islam
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 74
Deep Learning in Proteomics
Bo Wen, Wen‐Feng Zeng, Yuxing Liao, et al.
PROTEOMICS (2020) Vol. 20, Iss. 21-22
Open Access | Times Cited: 127
Improving protein succinylation sites prediction using embeddings from protein language model
Suresh Pokharel, Pawel Pratyush, Michael Heinzinger, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 45
Roles of protein post-translational modifications in glucose and lipid metabolism: mechanisms and perspectives
Yuhang Yang, Ri Wen, Ni Yang, et al.
Molecular Medicine (2023) Vol. 29, Iss. 1
Open Access | Times Cited: 31
Protein succinylation: regulating metabolism and beyond
Xiaoli Hou, Yiqiu Chen, Xiao Li, et al.
Frontiers in Nutrition (2024) Vol. 11
Open Access | Times Cited: 8
Comparison of Machine Learning and Deep Learning Models for Network Intrusion Detection Systems
Niraj Thapa, Zhipeng Liu, Dukka B. KC, et al.
Future Internet (2020) Vol. 12, Iss. 10, pp. 167-167
Open Access | Times Cited: 68
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
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
nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning
Yongzi Chen, Zhuozhi Wang, Yanan Wang, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Open Access | Times Cited: 39
Improving PTM Site Prediction by Coupling of Multi-Granularity Structure and Multi-Scale Sequence Representation
Zhengyi Li, Menglu Li, Lida Zhu, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2024) Vol. 38, Iss. 1, pp. 188-196
Open Access | Times Cited: 4
Insights on post-translational modifications in fatty liver and fibrosis progression
C. Nageswara Raju, Kavitha Sankaranarayanan
Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease (2025) Vol. 1871, Iss. 3, pp. 167659-167659
Closed Access
Integrating CNN and Bi-LSTM for protein succinylation sites prediction based on Natural Language Processing technique
Thi-Xuan Tran, Nguyen Quoc Khanh Le, Van-Nui Nguyen
Computers in Biology and Medicine (2025) Vol. 186, pp. 109664-109664
Closed Access
DeepRMethylSite: a deep learning based approach for prediction of arginine methylation sites in proteins
Meenal Chaudhari, Niraj Thapa, Kaushik Roy, et al.
Molecular Omics (2020) Vol. 16, Iss. 5, pp. 448-454
Open Access | Times Cited: 33
LSTMCNNsucc: A Bidirectional LSTM and CNN-Based Deep Learning Method for Predicting Lysine Succinylation Sites
Guohua Huang, Qingfeng Shen, Guiyang Zhang, et al.
BioMed Research International (2021) Vol. 2021, pp. 1-10
Open Access | Times Cited: 28
DeepNGlyPred: A Deep Neural Network-Based Approach for Human N-Linked Glycosylation Site Prediction
Subash C. Pakhrin, Kiyoko F. Aoki‐Kinoshita, Doina Caragea, et al.
Molecules (2021) Vol. 26, Iss. 23, pp. 7314-7314
Open Access | Times Cited: 28
Protein post-translational modification by lysine succinylation: Biochemistry, biological implications, and therapeutic opportunities
Zhao Guo, Junfeng Zhen, Xinyuan Liu, et al.
Genes & Diseases (2022) Vol. 10, Iss. 4, pp. 1242-1262
Open Access | Times Cited: 19
iRice-MS: An integrated XGBoost model for detecting multitype post-translational modification sites in rice
Hao Lv, Yang Zhang, Jia-Shu Wang, et al.
Briefings in Bioinformatics (2021) Vol. 23, Iss. 1
Closed Access | Times Cited: 23
UbiComb: A Hybrid Deep Learning Model for Predicting Plant-Specific Protein Ubiquitylation Sites
Arslan Siraj, Dae Yeong Lim, Hilal Tayara, et al.
Genes (2021) Vol. 12, Iss. 5, pp. 717-717
Open Access | Times Cited: 22
RecSNO: Prediction of Protein S-Nitrosylation Sites Using a Recurrent Neural Network
Arslan Siraj, Tuvshinbayar Chantsalnyam, Hilal Tayara, et al.
IEEE Access (2021) Vol. 9, pp. 6674-6682
Open Access | Times Cited: 20
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
Emerging trends in post-translational modification: Shedding light on Glioblastoma multiforme
Smita Kumari, Rohan Gupta, Rashmi K. Ambasta, et al.
Biochimica et Biophysica Acta (BBA) - Reviews on Cancer (2023) Vol. 1878, Iss. 6, pp. 188999-188999
Closed Access | Times Cited: 7
Impact of Lysine Succinylation on the Biology of Fungi
John Adejor, Elisabeth Tumukunde, Guoqi Li, et al.
Current Issues in Molecular Biology (2024) Vol. 46, Iss. 2, pp. 1020-1046
Open Access | Times Cited: 2
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
DeepDN_iGlu: prediction of lysine glutarylation sites based on attention residual learning method and DenseNet
Jianhua Jia, Mingwei Sun, Genqiang Wu, et al.
Mathematical Biosciences & Engineering (2022) Vol. 20, Iss. 2, pp. 2815-2830
Open Access | Times Cited: 12
Data-driven models for predicting intrinsically disordered protein polymer physics directly from composition or sequence
Tzu‐Hsuan Chao, Shiv Rekhi, Jeetain Mittal, et al.
Molecular Systems Design & Engineering (2023) Vol. 8, Iss. 9, pp. 1146-1155
Closed Access | Times Cited: 6
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