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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 prediction and analysis of species-specific fungi phosphorylation via feature optimization strategy
Man Cao, Guodong Chen, Jialin Yu, et al.
Briefings in Bioinformatics (2018) Vol. 21, Iss. 2, pp. 595-608
Closed Access | Times Cited: 15

Showing 15 citing articles:

A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis
Kasmika Borah, Himanish Shekhar Das, Soumita Seth, et al.
Functional & Integrative Genomics (2024) Vol. 24, Iss. 5
Closed Access | Times Cited: 10
Prediction of protein crotonylation sites through LightGBM classifier based on SMOTE and elastic net
Yaning Liu, Zhaomin Yu, Cheng Chen, et al.
Analytical Biochemistry (2020) Vol. 609, pp. 113903-113903
Closed Access | Times Cited: 69
Recent Advances in the Microbial Synthesis of Hemoglobin
Xinrui Zhao, Jingwen Zhou, Guocheng Du, et al.
Trends in biotechnology (2020) Vol. 39, Iss. 3, pp. 286-297
Closed Access | Times Cited: 57
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
Soy Leghemoglobin: A review of its structure, production, safety aspects, and food applications
Muhammad Ijaz Ahmad, Shahzad Farooq, Yasmin Alhamoud, et al.
Trends in Food Science & Technology (2023) Vol. 141, pp. 104199-104199
Closed Access | Times Cited: 9
Identifying Protein Phosphorylation Site-Disease Associations Based on Multi-Similarity Fusion and Negative Sample Selection by Convolutional Neural Network
Qian Deng, Jing Zhang, Jie Liu, et al.
Interdisciplinary Sciences Computational Life Sciences (2024) Vol. 16, Iss. 3, pp. 649-664
Closed Access | Times Cited: 2
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
Machine learning empowers phosphoproteome prediction in cancers
Hongyang Li, Yuanfang Guan
Bioinformatics (2019) Vol. 36, Iss. 3, pp. 859-864
Open Access | Times Cited: 14
GPS-PBS: A Deep Learning Framework to Predict Phosphorylation Sites that Specifically Interact with Phosphoprotein-Binding Domains
Yaping Guo, Wanshan Ning, Peiran Jiang, et al.
Cells (2020) Vol. 9, Iss. 5, pp. 1266-1266
Open Access | Times Cited: 13
mUSP: a high-accuracy map of thein situcrosstalk of ubiquitylation and SUMOylation proteome predicted via the feature enhancement approach
Haodong Xu, Ru‐Ping Liang, You‐Gan Wang, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 3
Closed Access | Times Cited: 11
ScerePhoSite: An interpretable method for identifying fungal phosphorylation sites in proteins using sequence-based features
Chao Wang, Qiang Yang
Computers in Biology and Medicine (2023) Vol. 158, pp. 106798-106798
Closed Access | Times Cited: 2
PKSPS: a novel method for predicting kinase of specific phosphorylation sites based on maximum weighted bipartite matching algorithm and phosphorylation sequence enrichment analysis
Xinyun Guo, Huan He, Jialin Yu, et al.
Briefings in Bioinformatics (2021) Vol. 23, Iss. 1
Closed Access | Times Cited: 4
MFPSP: Identification of fungal species-specific phosphorylation site using offspring competition-based genetic algorithm
Chao Wang, Quan Zou
PLoS Computational Biology (2024) Vol. 20, Iss. 11, pp. e1012607-e1012607
Open Access
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
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
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