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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 1-25 of 124 citing articles:

Artificial intelligence for precision medicine in neurodevelopmental disorders
Mohammed Uddin, Yujiang Wang, Marc Woodbury‐Smith
npj Digital Medicine (2019) Vol. 2, Iss. 1
Open Access | Times Cited: 196
ACP-DL: A Deep Learning Long Short-Term Memory Model to Predict Anticancer Peptides Using High-Efficiency Feature Representation
Hai-Cheng Yi, Zhu‐Hong You, Xi Zhou, et al.
Molecular Therapy — Nucleic Acids (2019) Vol. 17, pp. 1-9
Open Access | Times Cited: 182
Computational Drug Design Methods—Current and Future Perspectives
Fernando D. Prieto‐Martínez, Edgar López‐López, K. Eurídice Juárez‐Mercado, et al.
Elsevier eBooks (2019), pp. 19-44
Closed Access | Times Cited: 115
MLMDA: a machine learning approach to predict and validate MicroRNA–disease associations by integrating of heterogenous information sources
Kai Zheng, Zhu‐Hong You, Lei Wang, et al.
Journal of Translational Medicine (2019) Vol. 17, Iss. 1
Open Access | Times Cited: 112
In silico analyses on the comparative sensing of SARS‐CoV‐2 mRNA by the intracellular TLRs of humans
Abhigyan Choudhury, Nabarun Chandra Das, Ritwik Patra, et al.
Journal of Medical Virology (2021) Vol. 93, Iss. 4, pp. 2476-2486
Closed Access | Times Cited: 93
ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides
Sajid Ahmed, Rafsanjani Muhammod, Zahid Khan, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 69
Association predictions of genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, radiomics, drug, symptoms, environment factor, and disease networks: A comprehensive approach
Yi Pan, Xiujuan Lei, Yuchen Zhang
Medicinal Research Reviews (2021) Vol. 42, Iss. 1, pp. 441-461
Closed Access | Times Cited: 60
DeepMPF: deep learning framework for predicting drug–target interactions based on multi-modal representation with meta-path semantic analysis
Zhong-Hao Ren, Zhu‐Hong You, Quan Zou, et al.
Journal of Translational Medicine (2023) Vol. 21, Iss. 1
Open Access | Times Cited: 27
RNAincoder: a deep learning-based encoder for RNA and RNA-associated interaction
Yunxia Wang, Zhen Chen, Ziqi Pan, et al.
Nucleic Acids Research (2023) Vol. 51, Iss. W1, pp. W509-W519
Open Access | Times Cited: 25
A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson’s Disease
Domenico Buongiorno, Ilaria Bortone, Giacomo Donato Cascarano, et al.
BMC Medical Informatics and Decision Making (2019) Vol. 19, Iss. S9
Open Access | Times Cited: 76
Deep learning for mining protein data
Qiang Shi, Weiya Chen, Siqi Huang, et al.
Briefings in Bioinformatics (2019) Vol. 22, Iss. 1, pp. 194-218
Closed Access | Times Cited: 67
Recent methodology progress of deep learning for RNA–protein interaction prediction
Xiaoyong Pan, Yang Yang, Chunqiu Xia, et al.
Wiley Interdisciplinary Reviews - RNA (2019) Vol. 10, Iss. 6
Closed Access | Times Cited: 66
StackPDB: Predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier
Qingmei Zhang, Peishun Liu, Xue Wang, et al.
Applied Soft Computing (2020) Vol. 99, pp. 106921-106921
Open Access | Times Cited: 52
Protein–DNA/RNA interactions: Machine intelligence tools and approaches in the era of artificial intelligence and big data
Feifei Cui, Zilong Zhang, Chen Cao, et al.
PROTEOMICS (2022) Vol. 22, Iss. 8
Closed Access | Times Cited: 30
Deep learning and ensemble deep learning for circRNA-RBP interaction prediction in the last decade: A review
Dilan Lasantha, Sugandima Vidanagamachchi, Sam Nallaperuma
Engineering Applications of Artificial Intelligence (2023) Vol. 123, pp. 106352-106352
Closed Access | Times Cited: 20
MHAM-NPI: Predicting ncRNA-protein interactions based on multi-head attention mechanism
Zhecheng Zhou, Zhenya Du, Jinhang Wei, et al.
Computers in Biology and Medicine (2023) Vol. 163, pp. 107143-107143
Closed Access | Times Cited: 19
RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information
Hai-Cheng Yi, Zhu‐Hong You, Mei-Neng Wang, et al.
BMC Bioinformatics (2020) Vol. 21, Iss. 1
Open Access | Times Cited: 47
DeepDRBP-2L: A New Genome Annotation Predictor for Identifying DNA-Binding Proteins and RNA-Binding Proteins Using Convolutional Neural Network and Long Short-Term Memory
Jun Zhang, Qingcai Chen, Bin Liu
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2019) Vol. 18, Iss. 4, pp. 1451-1463
Closed Access | Times Cited: 44
LPI-CNNCP: Prediction of lncRNA-protein interactions by using convolutional neural network with the copy-padding trick
Shao‐Wu Zhang, Xixi Zhang, Xiao-Nan Fan, et al.
Analytical Biochemistry (2020) Vol. 601, pp. 113767-113767
Closed Access | Times Cited: 41
Capsule-LPI: a LncRNA–protein interaction predicting tool based on a capsule network
Ying Li, Hang Sun, Shiyao Feng, et al.
BMC Bioinformatics (2021) Vol. 22, Iss. 1
Open Access | Times Cited: 35
CapsNet-LDA: predicting lncRNA-disease associations using attention mechanism and capsule network based on multi-view data
Zequn Zhang, Junlin Xu, Yanan Wu, et al.
Briefings in Bioinformatics (2022) Vol. 24, Iss. 1
Closed Access | Times Cited: 27
HeadTailTransfer: An efficient sampling method to improve the performance of graph neural network method in predicting sparse ncRNA–protein interactions
Jinhang Wei, Linlin Zhuo, Shiyao Pan, et al.
Computers in Biology and Medicine (2023) Vol. 157, pp. 106783-106783
Closed Access | Times Cited: 15
RPI-CapsuleGAN: Predicting RNA-protein interactions through an interpretable generative adversarial capsule network
Yifei Wang, Xue Wang, Cheng Chen, et al.
Pattern Recognition (2023) Vol. 141, pp. 109626-109626
Closed Access | Times Cited: 14
Analysis of disease comorbidity patterns in a large-scale China population
Mengfei Guo, Yanan Yu, Tiancai Wen, et al.
BMC Medical Genomics (2019) Vol. 12, Iss. S12
Open Access | Times Cited: 42
Learning distributed representations of RNA and protein sequences and its application for predicting lncRNA-protein interactions
Hai-Cheng Yi, Zhu‐Hong You, Li Cheng, et al.
Computational and Structural Biotechnology Journal (2019) Vol. 18, pp. 20-26
Open Access | Times Cited: 41
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