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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:

Integration of A Deep Learning Classifier with A Random Forest Approach for Predicting Malonylation Sites
Zhen Chen, Ningning He, Yu Huang, et al.
Genomics Proteomics & Bioinformatics (2018) Vol. 16, Iss. 6, pp. 451-459
Open Access | Times Cited: 90

Showing 1-25 of 90 citing articles:

iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data
Zhen Chen, Pei Zhao, Fuyi Li, et al.
Briefings in Bioinformatics (2019) Vol. 21, Iss. 3, pp. 1047-1057
Closed Access | Times Cited: 369
iLearnPlus:a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization
Zhen Chen, Pei Zhao, Chen Li, et al.
Nucleic Acids Research (2021) Vol. 49, Iss. 10, pp. e60-e60
Open Access | Times Cited: 194
pAtbP-EnC: Identifying Anti-Tubercular Peptides Using Multi-Feature Representation and Genetic Algorithm-Based Deep Ensemble Model
Shahid Akbar, Ali Raza, Tamara Al Shloul, et al.
IEEE Access (2023) Vol. 11, pp. 137099-137114
Open Access | Times Cited: 53
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
Comprehensive review and assessment of computational methods for predicting RNA post-transcriptional modification sites from RNA sequences
Zhen Chen, Pei Zhao, Fuyi Li, et al.
Briefings in Bioinformatics (2019) Vol. 21, Iss. 5, pp. 1676-1696
Closed Access | Times Cited: 121
Machine learning method for energy consumption prediction of ships in port considering green ports
Yun Peng, Huan Liu, Xiangda Li, et al.
Journal of Cleaner Production (2020) Vol. 264, pp. 121564-121564
Closed Access | Times Cited: 90
Deep4mC: systematic assessment and computational prediction for DNA N4-methylcytosine sites by deep learning
Haodong Xu, Peilin Jia, Zhongming Zhao
Briefings in Bioinformatics (2020) Vol. 22, Iss. 3
Open Access | Times Cited: 73
iFeatureOmega:an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets
Zhen Chen, Xuhan Liu, Pei Zhao, et al.
Nucleic Acids Research (2022) Vol. 50, Iss. W1, pp. W434-W447
Open Access | Times Cited: 57
Machine learning-based models for the prediction of breast cancer recurrence risk
Duo Zuo, Lexin Yang, Yu Jin, et al.
BMC Medical Informatics and Decision Making (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 34
Urban nexus and transformative pathways towards a resilient Gauteng City-Region, South Africa
Luxon Nhamo, Lameck Rwizi, Sylvester Mpandeli, et al.
Cities (2021) Vol. 116, pp. 103266-103266
Open Access | Times Cited: 48
Identification of novel umami peptides from yeast extract and the mechanism against T1R1/T1R3
Hao Wang, Wenjun Wang, Shuyu Zhang, et al.
Food Chemistry (2023) Vol. 429, pp. 136807-136807
Closed Access | Times Cited: 17
Drug design targeting active posttranslational modification protein isoforms
Fanwang Meng, Zhongjie Liang, Kehao Zhao, et al.
Medicinal Research Reviews (2020) Vol. 41, Iss. 3, pp. 1701-1750
Closed Access | Times Cited: 42
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
Using Embedded Feature Selection and CNN for Classification on CCD-INID-V1—A New IoT Dataset
Zhipeng Liu, Niraj Thapa, Addison Shaver, et al.
Sensors (2021) Vol. 21, Iss. 14, pp. 4834-4834
Open Access | Times Cited: 38
A Combined Deep CNN: LSTM with a Random Forest Approach for Breast Cancer Diagnosis
Almas Begum, V. Dhilip Kumar, Junaid Asghar, et al.
Complexity (2022) Vol. 2022, Iss. 1
Open Access | Times Cited: 26
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
Compositional features analysis by machine learning in genome represents linear adaptation of monkeypox virus
Sen Zhang, Yadan Li, Yurong Cai, et al.
Frontiers in Genetics (2024) Vol. 15
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
ACP‐DPE: A Dual‐Channel Deep Learning Model for Anticancer Peptide Prediction
Guohua Huang, Yujie Cao, Qi Dai, et al.
IET Systems Biology (2025) Vol. 19, Iss. 1
Open Access
iPredCNC: Computational prediction model for cancerlectins and non-cancerlectins using novel cascade features subset selection
Zaheer Ullah Khan, Farman Ali, Irfan Ahmad, et al.
Chemometrics and Intelligent Laboratory Systems (2019) Vol. 195, pp. 103876-103876
Closed Access | Times Cited: 38
Standard Plane Identification in Fetal Brain Ultrasound Scans Using a Differential Convolutional Neural Network
Ruowei Qu, Guizhi Xu, Chunxia Ding, et al.
IEEE Access (2020) Vol. 8, pp. 83821-83830
Open Access | Times Cited: 37
DeepPPSite: A deep learning-based model for analysis and prediction of phosphorylation sites using efficient sequence information
Saeed Ahmed, Muhammad Kabir, Muhammad Arif, et al.
Analytical Biochemistry (2020) Vol. 612, pp. 113955-113955
Closed Access | Times Cited: 35
Identification of Protein Lysine Crotonylation Sites by a Deep Learning Framework With Convolutional Neural Networks
Yiming Zhao, Ningning He, Zhen Chen, et al.
IEEE Access (2020) Vol. 8, pp. 14244-14252
Open Access | Times Cited: 32
ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree
Yanjuan Li, Ma Di, Dong Chen, et al.
Frontiers in Genetics (2023) Vol. 14
Open Access | Times Cited: 12
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