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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 Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information
Hai-Cheng Yi, Zhu‐Hong You, De-Shuang Huang, et al.
Molecular Therapy — Nucleic Acids (2018) Vol. 11, pp. 337-344
Open Access | Times Cited: 124

Showing 26-50 of 124 citing articles:

Implementation of machine learning algorithms to create diabetic patient re-admission profiles
Mohamed Alloghani, Ahmed J. Aljaaf, Abir Hussain, et al.
BMC Medical Informatics and Decision Making (2019) Vol. 19, Iss. S9
Open Access | Times Cited: 39
Construction and Comprehensive Analysis of a Molecular Association Network via lncRNA–miRNA–Disease–Drug–Protein Graph
Zhen-Hao Guo, Hai-Cheng Yi, Zhu-Hong You
Cells (2019) Vol. 8, Iss. 8, pp. 866-866
Open Access | Times Cited: 38
Multi-feature fusion for deep learning to predict plant lncRNA-protein interaction
Jael Sanyanda Wekesa, Jun Meng, Yushi Luan
Genomics (2020) Vol. 112, Iss. 5, pp. 2928-2936
Open Access | Times Cited: 33
Bioinformatic Tools for the Analysis and Prediction of ncRNA Interactions
Andrés Rincón‐Riveros, Duvan Morales, Josefa Antonia Rodrı́guez, et al.
International Journal of Molecular Sciences (2021) Vol. 22, Iss. 21, pp. 11397-11397
Open Access | Times Cited: 31
EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA–protein interaction prediction
Jingjing Wang, Yanpeng Zhao, Weikang Gong, et al.
BMC Bioinformatics (2021) Vol. 22, Iss. 1
Open Access | Times Cited: 29
Extra Trees Method for Predicting LncRNA-Disease Association Based On Multi-Layer Graph Embedding Aggregation
Qing-Wen Wu, Ruifen Cao, Junfeng Xia, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2021) Vol. 19, Iss. 6, pp. 3171-3178
Closed Access | Times Cited: 28
Targeting ST8SIA6-AS1 counteracts KRASG12C inhibitor resistance through abolishing the reciprocal activation of PLK1/c-Myc signaling
Ya‐Fang Wang, Mingyue Yao, Cheng Li, et al.
Experimental Hematology and Oncology (2023) Vol. 12, Iss. 1
Open Access | Times Cited: 13
The roles of lncRNAs and miRNAs in pancreatic cancer: a focus on cancer development and progression and their roles as potential biomarkers
Somayeh Jafari, Hossein Motedayyen, Parisa Javadi, et al.
Frontiers in Oncology (2024) Vol. 14
Open Access | Times Cited: 4
A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images
Vitoantonio Bevilacqua, Antonio Brunetti, Giacomo Donato Cascarano, et al.
BMC Medical Informatics and Decision Making (2019) Vol. 19, Iss. S9
Open Access | Times Cited: 35
Accurate Prediction of ncRNA-Protein Interactions From the Integration of Sequence and Evolutionary Information
Zhaohui Zhan, Zhu‐Hong You, Liping Li, et al.
Frontiers in Genetics (2018) Vol. 9
Open Access | Times Cited: 33
Prediction of Self-Interacting Proteins from Protein Sequence Information Based on Random Projection Model and Fast Fourier Transform
Zhan‐Heng Chen, Zhu‐Hong You, Liping Li, et al.
International Journal of Molecular Sciences (2019) Vol. 20, Iss. 4, pp. 930-930
Open Access | Times Cited: 31
An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network
Han-Jing Jiang, Yabing Huang
BMC Bioinformatics (2022) Vol. 23, Iss. 1
Open Access | Times Cited: 19
ncRPI-LGAT: Prediction of ncRNA-protein interactions with line graph attention network framework
Yong Han, Shao‐Wu Zhang
Computational and Structural Biotechnology Journal (2023) Vol. 21, pp. 2286-2295
Open Access | Times Cited: 10
Validate association of gene loci and establish genetic risk prediction models for late-onset Alzheimer's disease in Chinese populations
F. Li, Menghan Zheng, Jianping Jia
Journal of Alzheimer s Disease (2025)
Closed Access
A Hybrid Prediction Method for Plant lncRNA-Protein Interaction
Jael Sanyanda Wekesa, Yushi Luan, Ming Chen, et al.
Cells (2019) Vol. 8, Iss. 6, pp. 521-521
Open Access | Times Cited: 28
Using Weighted Extreme Learning Machine Combined With Scale-Invariant Feature Transform to Predict Protein-Protein Interactions From Protein Evolutionary Information
Jianqiang Li, Xiaofeng Shi, Zhu‐Hong You, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2020) Vol. 17, Iss. 5, pp. 1546-1554
Closed Access | Times Cited: 27
Adaptive multi-source multi-view latent feature learning for inferring potential disease-associated miRNAs
Qiu Xiao, Ning Zhang, Jiawei Luo, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 2, pp. 2043-2057
Closed Access | Times Cited: 27
Computational predictive approaches for interaction and structure of aptamers
Neda Emami, Parvin Samadi Pakchin, Reza Ferdousi
Journal of Theoretical Biology (2020) Vol. 497, pp. 110268-110268
Closed Access | Times Cited: 27
Learning Representations to Predict Intermolecular Interactions on Large-Scale Heterogeneous Molecular Association Network
Hai-Cheng Yi, Zhu‐Hong You, De-Shuang Huang, et al.
iScience (2020) Vol. 23, Iss. 7, pp. 101261-101261
Open Access | Times Cited: 25
DeepLPI: a multimodal deep learning method for predicting the interactions between lncRNAs and protein isoforms
Dipan Shaw, Hao Chen, Minzhu Xie, et al.
BMC Bioinformatics (2021) Vol. 22, Iss. 1
Open Access | Times Cited: 23
Predicting Protein Interactions Using a Deep Learning Method-Stacked Sparse Autoencoder Combined with a Probabilistic Classification Vector Machine
Yanbin Wang, Zhu‐Hong You, Liping Li, et al.
Complexity (2018) Vol. 2018, Iss. 1
Open Access | Times Cited: 25
A survey of current trends in computational predictions of protein-protein interactions
Yanbin Wang, Zhu‐Hong You, Liping Li, et al.
Frontiers of Computer Science (2020) Vol. 14, Iss. 4
Closed Access | Times Cited: 23
Learning Representation of Molecules in Association Network for Predicting Intermolecular Associations
Hai-Cheng Yi, Zhu‐Hong You, Zhen-Hao Guo, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2020) Vol. 18, Iss. 6, pp. 2546-2554
Open Access | Times Cited: 23
A learning based framework for diverse biomolecule relationship prediction in molecular association network
Zhen-Hao Guo, Zhu‐Hong You, De-Shuang Huang, et al.
Communications Biology (2020) Vol. 3, Iss. 1
Open Access | Times Cited: 23
Using Evolutionary Information and Multi-Label Linear Discriminant Analysis to Predict the Subcellular Location of Multi-Site Bacterial Proteins via Chou’s 5-Steps Rule
Lei Du, Qingfang Meng, Hui Jiang, et al.
IEEE Access (2020) Vol. 8, pp. 56452-56461
Open Access | Times Cited: 21

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