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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:

Reduction strategies for hierarchical multi-label classification in protein function prediction
Ricardo Cerri, Rodrigo C. Barros, André C. P. L. F. de Carvalho, et al.
BMC Bioinformatics (2016) Vol. 17, Iss. 1
Open Access | Times Cited: 100

Showing 1-25 of 100 citing articles:

Machine learning techniques for protein function prediction
Rosalin Bonetta, Gianluca Valentino
Proteins Structure Function and Bioinformatics (2019) Vol. 88, Iss. 3, pp. 397-413
Closed Access | Times Cited: 132
Hierarchical Multi-label Text Classification
Wei Huang, Enhong Chen, Qi Liu, et al.
(2019)
Closed Access | Times Cited: 121
PFmulDL: a novel strategy enabling multi-class and multi-label protein function annotation by integrating diverse deep learning methods
Weiqi Xia, Lingyan Zheng, Jiebin Fang, et al.
Computers in Biology and Medicine (2022) Vol. 145, pp. 105465-105465
Closed Access | Times Cited: 59
Movie genre classification: A multi-label approach based on convolutions through time
Jônatas Wehrmann, Rodrigo C. Barros
Applied Soft Computing (2017) Vol. 61, pp. 973-982
Closed Access | Times Cited: 84
Label Relation Graphs Enhanced Hierarchical Residual Network for Hierarchical Multi-Granularity Classification
Jingzhou Chen, Peng Wang, Jian Liu, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022), pp. 4848-4857
Open Access | Times Cited: 36
Hierarchical-taxonomy-aware and attentional convolutional neural networks for acoustic identification of bird species: A phylogenetic perspective
Qingyu Wang, Yanzhi Song, Yeqian Du, et al.
Ecological Informatics (2024) Vol. 80, pp. 102538-102538
Open Access | Times Cited: 6
Ontological function annotation of long non-coding RNAs through hierarchical multi-label classification
Jingpu Zhang, Zuping Zhang, Zixiang Wang, et al.
Bioinformatics (2017) Vol. 34, Iss. 10, pp. 1750-1757
Open Access | Times Cited: 54
CHEER: HierarCHical taxonomic classification for viral mEtagEnomic data via deep leaRning
Jiayu Shang, Yanni Sun
Methods (2020) Vol. 189, pp. 95-103
Open Access | Times Cited: 46
LA-HCN: Label-based Attention for Hierarchical Multi-label Text Classification Neural Network
Xinyi Zhang, Jiahao Xu, Charlie Soh, et al.
Expert Systems with Applications (2021) Vol. 187, pp. 115922-115922
Open Access | Times Cited: 38
Top-down strategies for hierarchical classification of transposable elements with neural networks
Felipe Kenji Nakano, Walter José G. S. Pinto, Gisele L. Pappa, et al.
2022 International Joint Conference on Neural Networks (IJCNN) (2017), pp. 2539-2546
Closed Access | Times Cited: 41
Hierarchical Text Classification with Reinforced Label Assignment
Yuning Mao, Jingjing Tian, Jiawei Han, et al.
(2019)
Open Access | Times Cited: 40
Multi-granularity classification of upper gastrointestinal endoscopic images
Wei Hua, Do-Joon Yi, Shuyu Hu, et al.
Neurocomputing (2025), pp. 129564-129564
Closed Access
Semi-Supervised Hierarchical Multi-Label Classifier Based on Local Information
Jonathan Serrano-Pérez, L. Enrique Sucar
International Journal of Approximate Reasoning (2025), pp. 109411-109411
Closed Access
Reliable recommendations for CCTV sewer inspections through multi-label image classification
Rémi Cuingnet, Marine Bernard, Phillipe R. Sampaio, et al.
Advanced Engineering Informatics (2025) Vol. 65, pp. 103317-103317
Closed Access
Active learning for hierarchical multi-label classification
Felipe Kenji Nakano, Ricardo Cerri, Celine Vens
Data Mining and Knowledge Discovery (2020) Vol. 34, Iss. 5, pp. 1496-1530
Open Access | Times Cited: 28
CCN+: A neuro-symbolic framework for deep learning with requirements
Eleonora Giunchiglia, Alex Tatomir, Mihaela Cătălina Stoian, et al.
International Journal of Approximate Reasoning (2024) Vol. 171, pp. 109124-109124
Open Access | Times Cited: 3
Improving protein function prediction using protein sequence and GO-term similarities
Stavros Makrodimitris, Roeland C. H. J. van Ham, Marcel J. T. Reinders
Bioinformatics (2018) Vol. 35, Iss. 7, pp. 1116-1124
Open Access | Times Cited: 31
Incremental Class Learning for Hierarchical Classification
Ju-Youn Park, Jong-Hwan Kim
IEEE Transactions on Cybernetics (2018) Vol. 50, Iss. 1, pp. 178-189
Closed Access | Times Cited: 29
Machine learning for discovering missing or wrong protein function annotations
Felipe Kenji Nakano, Mathias Lietaert, Celine Vens
BMC Bioinformatics (2019) Vol. 20, Iss. 1
Open Access | Times Cited: 26
A hierarchical multi-label classification method based on neural networks for gene function prediction
Shou Feng, Ping Fu, Wenbin Zheng
Biotechnology & Biotechnological Equipment (2018) Vol. 32, Iss. 6, pp. 1613-1621
Open Access | Times Cited: 25
Inducing Hierarchical Multi-label Classification rules with Genetic Algorithms
Ricardo Cerri, Márcio P. Basgalupp, Rodrigo C. Barros, et al.
Applied Soft Computing (2019) Vol. 77, pp. 584-604
Closed Access | Times Cited: 24
ClassifyTE: a stacking-based prediction of hierarchical classification of transposable elements
Manisha Panta, Avdesh Mishra, Md Tamjidul Hoque, et al.
Bioinformatics (2021) Vol. 37, Iss. 17, pp. 2529-2536
Closed Access | Times Cited: 19
Stacking Methods for Hierarchical Classification
Felipe Kenji Nakano, Saulo Martiello Mastelini, Sylvio Barbon, et al.
2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) (2017)
Closed Access | Times Cited: 24
Improving Hierarchical Classification of Transposable Elements using Deep Neural Networks
Felipe Kenji Nakano, Saulo Martiello Mastelini, Sylvio Barbon, et al.
2022 International Joint Conference on Neural Networks (IJCNN) (2018), pp. 1-8
Closed Access | Times Cited: 22
Multi-Label Classification Neural Networks with Hard Logical Constraints
Eleonora Giunchiglia, Thomas Lukasiewicz
Journal of Artificial Intelligence Research (2021) Vol. 72, pp. 759-818
Open Access | Times Cited: 16
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