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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:

iN6-methylat (5-step): identifying DNA N6-methyladenine sites in rice genome using continuous bag of nucleobases via Chou’s 5-step rule
Nguyen Quoc Khanh Le
Molecular Genetics and Genomics (2019) Vol. 294, Iss. 5, pp. 1173-1182
Closed Access | Times Cited: 82

Showing 1-25 of 82 citing articles:

SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome
Shaherin Basith, Balachandran Manavalan, Tae Hwan Shin, et al.
Molecular Therapy — Nucleic Acids (2019) Vol. 18, pp. 131-141
Open Access | Times Cited: 151
Classifying Promoters by Interpreting the Hidden Information of DNA Sequences via Deep Learning and Combination of Continuous FastText N-Grams
Nguyen Quoc Khanh Le, Edward Kien Yee Yapp, N. Nagasundaram, et al.
Frontiers in Bioengineering and Biotechnology (2019) Vol. 7
Open Access | Times Cited: 104
Using deep neural networks and biological subwords to detect protein S-sulfenylation sites
Duyen Thi, Thanh Quynh Trang Le, Nguyen Quoc Khanh Le
Briefings in Bioinformatics (2020) Vol. 22, Iss. 3
Closed Access | Times Cited: 84
Deep transformers and convolutional neural network in identifying DNA N6-methyladenine sites in cross-species genomes
Nguyen Quoc Khanh Le, Quang‐Thai Ho
Methods (2021) Vol. 204, pp. 199-206
Closed Access | Times Cited: 81
XG-PseU: an eXtreme Gradient Boosting based method for identifying pseudouridine sites
Kewei Liu, Wei Chen, Hao Lin
Molecular Genetics and Genomics (2019) Vol. 295, Iss. 1, pp. 13-21
Closed Access | Times Cited: 67
A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification
Nguyen Quoc Khanh Le, Duyen Thi, Truong Nguyen Khanh Hung, et al.
International Journal of Molecular Sciences (2020) Vol. 21, Iss. 23, pp. 9070-9070
Open Access | Times Cited: 62
A Convolutional Neural Network Using Dinucleotide One-hot Encoder for identifying DNA N6-Methyladenine Sites in the Rice Genome
Zhibin Lv, Hui Ding, Lei Wang, et al.
Neurocomputing (2020) Vol. 422, pp. 214-221
Closed Access | Times Cited: 60
Using extreme gradient boosting to identify origin of replication in Saccharomyces cerevisiae via hybrid features
Duyen Thi, Nguyen Quoc Khanh Le
Genomics (2020) Vol. 112, Iss. 3, pp. 2445-2451
Open Access | Times Cited: 58
Identifying SNAREs by Incorporating Deep Learning Architecture and Amino Acid Embedding Representation
Nguyen Quoc Khanh Le, Tuân-Tú Huỳnh
Frontiers in Physiology (2019) Vol. 10
Open Access | Times Cited: 54
Pep-CNN: An improved convolutional neural network for predicting therapeutic peptides
Shengli Zhang, Xinjie Li
Chemometrics and Intelligent Laboratory Systems (2022) Vol. 221, pp. 104490-104490
Closed Access | Times Cited: 23
dPromoter-XGBoost: Detecting promoters and strength by combining multiple descriptors and feature selection using XGBoost
Hongfei Li, Lei Shi, Wentao Gao, et al.
Methods (2022) Vol. 204, pp. 215-222
Open Access | Times Cited: 23
iPro-TCN: Prediction of DNA Promoters Recognition and Their Strength Using Temporal Convolutional Network
Ali Raza, Waleed Alam, Shahzad Khan, et al.
IEEE Access (2023) Vol. 11, pp. 66113-66121
Open Access | Times Cited: 13
DNA sequence analysis landscape: a comprehensive review of DNA sequence analysis task types, databases, datasets, word embedding methods, and language models
Muhammad Nabeel Asim, Muhammad Ali Ibrahim, Alam Zaib, et al.
Frontiers in Medicine (2025) Vol. 12
Open Access
Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks
Bing Niu, Chaofeng Liang, Yi Lu, et al.
Genomics (2019) Vol. 112, Iss. 1, pp. 837-847
Open Access | Times Cited: 40
Impacts of Pseudo Amino Acid Components and 5-steps Rule to Proteomics and Proteome Analysis
Kuo‐Chen Chou
Current Topics in Medicinal Chemistry (2019) Vol. 19, Iss. 25, pp. 2283-2300
Closed Access | Times Cited: 39
Classification of Infected Necrotizing Pancreatitis for Surgery Within or Beyond 4 Weeks Using Machine Learning
Lan Lan, Qiang Guo, Zhigang Zhang, et al.
Frontiers in Bioengineering and Biotechnology (2020) Vol. 8
Open Access | Times Cited: 29
GT-Finder: Classify the family of glucose transporters with pre-trained BERT language models
Syed Muazzam Ali Shah, Semmy Wellem Taju, Quang‐Thai Ho, et al.
Computers in Biology and Medicine (2021) Vol. 131, pp. 104259-104259
Closed Access | Times Cited: 26
i6mA-Vote: Cross-Species Identification of DNA N6-Methyladenine Sites in Plant Genomes Based on Ensemble Learning With Voting
Zhixia Teng, Zhengnan Zhao, Yanjuan Li, et al.
Frontiers in Plant Science (2022) Vol. 13
Open Access | Times Cited: 16
Calcium Pattern Assessment in Patients with Severe Aortic Stenosis Via the Chou’s 5-Steps Rule
Agata Wiktorowicz, Adrian Wit, Artur Dziewierz, et al.
Current Pharmaceutical Design (2019) Vol. 25, Iss. 35, pp. 3769-3775
Closed Access | Times Cited: 27
Use Chou’s 5-Steps Rule to Predict Remote Homology Proteins by Merging Grey Incidence Analysis and Domain Similarity Analysis
Wei‐Zhong Lin, Xuan Xiao, Wang‐Ren Qiu, et al.
Natural Science (2020) Vol. 12, Iss. 03, pp. 181-198
Open Access | Times Cited: 25
A prediction modeling based on SNOT-22 score for endoscopic nasal septoplasty: a retrospective study
Xueran Kang, Bin Chen, Yisheng Chen, et al.
PeerJ (2020) Vol. 8, pp. e9890-e9890
Open Access | Times Cited: 24
Research Progress in Predicting DNA Methylation Modifications and the Relation with Human Diseases
Chunyan Ao, Lin Gao, Liang Yu
Current Medicinal Chemistry (2021) Vol. 29, Iss. 5, pp. 822-836
Closed Access | Times Cited: 22
MGF6mARice: prediction of DNA N6-methyladenine sites in rice by exploiting molecular graph feature and residual block
Mengya Liu, Zhan-Li Sun, Zhigang Zeng, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 3
Closed Access | Times Cited: 13
Proposing 5-Steps Rule Is a Notable Milestone for Studying Molecular Biology
Kuo‐Chen Chou
Natural Science (2020) Vol. 12, Iss. 03, pp. 74-79
Open Access | Times Cited: 19
i6mA-DNC: Prediction of DNA N6-Methyladenosine sites in rice genome based on dinucleotide representation using deep learning
Sehi Park, Abdul Wahab, Iman Nazari, et al.
Chemometrics and Intelligent Laboratory Systems (2020) Vol. 204, pp. 104102-104102
Closed Access | Times Cited: 19
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