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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 novel features ranking metric with application to scalable visual and bioinformatics data classification
Quan Zou, Jiancang Zeng, Liujuan Cao, et al.
Neurocomputing (2015) Vol. 173, pp. 346-354
Closed Access | Times Cited: 378

Showing 1-25 of 378 citing articles:

Feature selection in machine learning: A new perspective
Jie Cai, Jiawei Luo, Shulin Wang, et al.
Neurocomputing (2018) Vol. 300, pp. 70-79
Closed Access | Times Cited: 1720
Predicting Diabetes Mellitus With Machine Learning Techniques
Quan Zou, Kaiyang Qu, Yamei Luo, et al.
Frontiers in Genetics (2018) Vol. 9
Open Access | Times Cited: 753
Performance of machine-learning scoring functions in structure-based virtual screening
Maciej Wójcikowski, Pedro J. Ballester, Paweł Siedlecki
Scientific Reports (2017) Vol. 7, Iss. 1
Open Access | Times Cited: 337
Tumor origin detection with tissue-specific miRNA and DNA methylation markers
Wei Tang, Shixiang Wan, Zhen Yang, et al.
Bioinformatics (2017) Vol. 34, Iss. 3, pp. 398-406
Open Access | Times Cited: 323
Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database
Joon Yul Choi, Tae Keun Yoo, Jeong Gi Seo, et al.
PLoS ONE (2017) Vol. 12, Iss. 11, pp. e0187336-e0187336
Open Access | Times Cited: 251
Deep-AmPEP30: Improve Short Antimicrobial Peptides Prediction with Deep Learning
Jielu Yan, Pratiti Bhadra, Ang Li, et al.
Molecular Therapy — Nucleic Acids (2020) Vol. 20, pp. 882-894
Open Access | Times Cited: 219
iTerm-PseKNC: a sequence-based tool for predicting bacterial transcriptional terminators
Chao-Qin Feng, Zhao‐Yue Zhang, Xiaojuan Zhu, et al.
Bioinformatics (2018) Vol. 35, Iss. 9, pp. 1469-1477
Closed Access | Times Cited: 206
i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome
Wei Chen, Hao Lv, Fulei Nie, et al.
Bioinformatics (2019) Vol. 35, Iss. 16, pp. 2796-2800
Closed Access | Times Cited: 196
CPPred-RF: A Sequence-based Predictor for Identifying Cell-Penetrating Peptides and Their Uptake Efficiency
Leyi Wei, Pengwei Xing, Ran Su, et al.
Journal of Proteome Research (2017) Vol. 16, Iss. 5, pp. 2044-2053
Closed Access | Times Cited: 193
Predicting protein structural classes for low-similarity sequences by evaluating different features
Xiaojuan Zhu, Chao-Qin Feng, Hong-Yan Lai, et al.
Knowledge-Based Systems (2018) Vol. 163, pp. 787-793
Closed Access | Times Cited: 192
Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities
Arshia Rehman, Saeeda Naz, Imran Razzak
Multimedia Systems (2021) Vol. 28, Iss. 4, pp. 1339-1371
Closed Access | Times Cited: 172
M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning
Leyi Wei, Huangrong Chen, Ran Su
Molecular Therapy — Nucleic Acids (2018) Vol. 12, pp. 635-644
Open Access | Times Cited: 170
Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species
Leyi Wei, Shasha Luan, Luís Augusto Eijy Nagai, et al.
Bioinformatics (2018) Vol. 35, Iss. 8, pp. 1326-1333
Closed Access | Times Cited: 169
SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting
Bin Yu, Wenying Qiu, Cheng Chen, et al.
Bioinformatics (2019) Vol. 36, Iss. 4, pp. 1074-1081
Open Access | Times Cited: 169
iProEP: A Computational Predictor for Predicting Promoter
Hong-Yan Lai, Zhao‐Yue Zhang, Zhendong Su, et al.
Molecular Therapy — Nucleic Acids (2019) Vol. 17, pp. 337-346
Open Access | Times Cited: 153
ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides
B. Dharma Rao, Chen Zhou, Guoying Zhang, et al.
Briefings in Bioinformatics (2019) Vol. 21, Iss. 5, pp. 1846-1855
Closed Access | Times Cited: 147
POSREG: proteomic signature discovered by simultaneously optimizing its reproducibility and generalizability
Fengcheng Li, Ying Zhou, Ying Zhang, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 2
Closed Access | Times Cited: 99
Prediction of protein solubility based on sequence physicochemical patterns and distributed representation information with DeepSoluE
Chao Wang, Quan Zou
BMC Biology (2023) Vol. 21, Iss. 1
Open Access | Times Cited: 51
Oral_voting_transfer: classification of oral microorganisms’ function proteins with voting transfer model
Wenzheng Bao, Yujun Liu, Baitong Chen
Frontiers in Microbiology (2024) Vol. 14
Open Access | Times Cited: 20
Identification of immunoglobulins using Chou's pseudo amino acid composition with feature selection technique
Hua Tang, Wei Chen, Hao Lin
Molecular BioSystems (2016) Vol. 12, Iss. 4, pp. 1269-1275
Closed Access | Times Cited: 169
Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy
Quan Zou, Shixiang Wan, Ying Ju, et al.
BMC Systems Biology (2016) Vol. 10, Iss. S4
Open Access | Times Cited: 155
Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools
Ran Su, Jie Hu, Quan Zou, et al.
Briefings in Bioinformatics (2018) Vol. 21, Iss. 2, pp. 408-420
Closed Access | Times Cited: 148
iRNA-2OM: A Sequence-Based Predictor for Identifying 2′-O-Methylation Sites inHomo sapiens
Hui Yang, Hao Lv, Hui Ding, et al.
Journal of Computational Biology (2018) Vol. 25, Iss. 11, pp. 1266-1277
Open Access | Times Cited: 141
Identification of hormone binding proteins based on machine learning methods
Jiu-Xin Tan, Shi-Hao Li, Zimei Zhang, et al.
Mathematical Biosciences & Engineering (2019) Vol. 16, Iss. 4, pp. 2466-2480
Open Access | Times Cited: 135
Developing a Multi-Dose Computational Model for Drug-Induced Hepatotoxicity Prediction Based on Toxicogenomics Data
Ran Su, Huichen Wu, Bo Xu, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2018) Vol. 16, Iss. 4, pp. 1231-1239
Closed Access | Times Cited: 128
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